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Erwin: A Tree-based Hierarchical Transformer for Large-scale Physical Systems | Accept (poster) | Summary: **Review updates after rebuttal**
The authors correspond to many of my commets well. As my initial score is already the positive side and I find it fair, hence I will keep my score unchanged.
**End Review updates after rebuttal**
The paper introduces Erwin, a hierarchical transformer that combines ball tree p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback and hope to address their concerns with the rebuttal.
## Experimental Designs Or Analyses
> Not all baselines are applied to all datasets. For example, PointTransformer v3 is not included in the fluid dynamics experiments, and EAGLE is not incl... | Summary: The authors propose a tree-based hierarchical transformer model to capture large-scale and small-scale interactions in exhaustively large datasets. The motivation is that traditional transformers are not scalable and efficient, so the authors propose a hierarchical model to allow course granting to capture lar... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We shall address their questions below. When discussing the questions regarding the cosmology dataset, we will refer to the original dataset paper by Balla et al.
## Questions
> "The input is a point cloud $X \in \mathbb{R}^{5000 \times 3}$" -> ... | Summary: The paper introduces Erwin, a hierarchical transformer model designed for large-scale physical systems. The model employs a ball tree partitioning strategy to group nodes, enabling linear-time attention by processing local neighborhoods in parallel. This hierarchical approach allows progressive coarsening and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the actionable feedback. We address their concerns and questions in the following.
## Questions For Authors
### Q1
That is an insightful question. In general, ball trees are geometry-adaptive but do not guarantee that balls will cover points in a perfectly natural way a... | Summary: The paper introduces a transformer-based approach for processing point cloud data. It utilizes ball-tree-based structures, enabling the attention-based framework to operate in linear time rather than quadratic time. This also allows the model to capture interactions at both fine and coarse scales.
The propose... | Rebuttal 1:
Rebuttal: We thank the reviewer for their encouraging feedback. We address their questions below.
## Questions For Authors
> The paper mentions "The code is available at anonymized link". But there seems to be no link attached at the "anonymized link".
We apologize for the inconvenience. An anonymized ver... | null | null | null | null | null | null |
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination’s Impact on Machine Translation | Accept (poster) | Summary: This paper presents a controlled study on the impact of data contamination on machine translation. They decontaminate their train-test splits and train two decoder-only models of different sizes (with 1 and 8 billion parameters). Then, they add test data into the pretraining data and train a contaminated model... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review. We will extend the discussion section to include a discussion on the broader implications of our findings.
On the choice of BLEU as a metric: We acknowledge that a string-based metric (like BLEU) has limitations. For this reason, we already accom... | Summary: This paper investigates the impact of data contamination on machine translation. In particular, the paper tests factors including source contamination, target contamination and temporal distribution.
Claims And Evidence: The claims are supported by abundant experiments from multiple data source.
Methods And ... | Rebuttal 1:
Rebuttal: Thanks a lot for your review of our paper.
First, we respectfully disagree that the contribution of our paper is insignificant. The key assumption of data contamination is motivated by the hypothesis that consuming test data leads to an overestimation of a model’s performance. While intuitive, th... | Summary: This paper presents a controlled large-scale study on how data contamination impacts machine translation evaluation in large language models. The researchers created a carefully decontaminated train-test split, then systematically reintroduced contamination under controlled conditions across different modes, t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review and insightful comments. We will use the reviewer’s feedback to revise the writing of Section 3.4 and include missing citations.
1. Details on contamination score calculation: The contamination score is calculated as the number of tokens in the longe... | Summary: The paper analyzes the influence of data contamination on LLMs trained for machine translation. Their testing controls factors such as the modes of contamination, the temporal distribution of contaminated samples, and the frequency the contaminated samples are presented. From their experimentation they demon... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed comments and feedback. We respond to the reviewer’s comments below.
1. Question 1 (training compute): All models are implemented as continued pre-training starting off baseline checkpoints. For instance, models with late contamination are only trained for 1... | null | null | null | null | null | null |
Scaling Sparse Feature Circuits For Studying In-Context Learning | Accept (poster) | Summary: This paper aim to understand the mechanism of ICL by leveraging SAE, along with other techniques such as ITO and SFC, to analyze the properties of ICL task vectors. And how these techniques can be combined and improved to enhance the understanding underlying mechanisms.
Claims And Evidence: I have few questi... | Rebuttal 1:
Rebuttal: Hello Reviewer `1tPd`, thank you for your review. We appreciate your time and feedback, but it appears there may be some fundamental misunderstandings about our paper's focus and contributions that we'd like to clarify. **You stated: “1. What is the main contribution of this paper? The paper attem... | Summary: This paper uses sparse autoencoders (SAEs) to study in-context learning (ICL), specifically focusing on ICL tasks that can be abstracted into a task vector. The authors propose task vector cleaning (TVC), a methodology to identify task-execution features from the set of SAE features that implement the ICL task... | Rebuttal 1:
Rebuttal: Hello Reviewer `fzas`. Thank you for your impressively extensive and thoughtful feedback, and for your careful attention to details even in the Appendix.
**On our "10-35x more parameters" claim:** You note that "[1] has already performed circuit-style analysis [...] using models up to 70B paramet... | Summary: The paper applies sparse autoencoders (SAE) to better understand in-context learning (ICL).
The paper first learns SAE representations of task vectors. Task vectors are first constructed in a heuristic manner (averaging the residual streams of arrow tokens (between inputs and outputs). The paper proposes "tas... | Rebuttal 1:
Rebuttal: Hello reviewer `zx2e` – thank you for your thoughtful review. We appreciate your recognition that our methods are "solid" and that task vector cleaning is "a simple approach that appears to work well to get strong SAE decompositions."
**Regarding feature definitions**: we consider *task-specific ... | Summary: This paper explores how sparse autoencoders (SAEs) can enhance our understanding of in-context learning (ICL) mechanisms in LLMs. The paper's main contributions include:
- Identifying two core components of ICL circuits: task-detection features that identify required tasks from the prompt, and task-execution f... | Rebuttal 1:
Rebuttal: Hello reviewer K3Cy,
Thank you for your thoughtful and supportive review of our paper. We appreciate your detailed feedback and are grateful for your recommendation to accept the paper.
Regarding your specific question about the weak detection-execution connections for the `person_profession` an... | null | null | null | null | null | null |
Efficiently Vectorized MCMC on Modern Accelerators | Accept (spotlight poster) | Summary: With the advancement of AI infrastructures, it is increasingly interesting to scale algorithms up with parallelism. Nevertheless, it is not efficient to run parallel MCMC with naive automatic vectorization (e.g., vmap), due to a varying execution time at each sampling step across different chains. The current ... | Rebuttal 1:
Rebuttal: Thank you for your diligent review - we are glad you believe our method is a worthwhile addition to modern MCMC libraries. Below we address your questions.
**Theoretical framework:** Eq(3) and eq(4) hold without taking expectations under appropriate theoretical conditions, but we appreciate the s... | Summary: This work focuses on minimizing synchronization steps when vectorizing MCMC algorithms. Modern MCMC methods, such as NUTS, involve stochastic number of operations per Markovian transition, depending on the initial sample and random seed. This introduces unavoidable overhead when running multiple chains in a ve... | Rebuttal 1:
Rebuttal: Thank you for your detailed and engaging review, we are excited that you see the value in our contribution. Below we address your comments and questions.
**FSM Efficiency gain:** The main reasons that the improvement in walltimes don’t match iters/sample in Fig 6 are (i) as you guessed, the slowe... | Summary: Vectorizing Markov Chain Monte Carlo (MCMC) algorithms using vmap to create multi chain code results in a synchronization problem. That is all chains have to wait for the last chain to be completed. The paper proposes to solve this problem by using FSMs, finite state machines, to design single-chain MCMC algor... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work and for your recommendations to improve the paper. Below we address your comments and questions.
**More general while loop structures:** The conversion of algorithms into FSMs by splitting up while loops is a generally applicable recipe, but we a... | Summary: This work proposes a way of more effectively parallelising (multiple chains of) certain MCMC algorithms via automatic vectorisation tools like the vmap function in JAX. Specifically, this work is concerned with MCMC algorithms whose computational cost per iteration has a large variance, e.g., due to the use of... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and recommendations. We are glad that you see the value of our contribution and your feedback has been helpful in strengthening the clarity of the paper. Below we address your main questions/comments:
**On step bundling**: In short, performance should always be... | null | null | null | null | null | null |
Towards Better-than-2 Approximation for Constrained Correlation Clustering | Accept (spotlight poster) | Summary: In this paper, the authors study Constrained Correlation Clustering, introduced by van Zuylen and Williamson (2009). The problem is a generalization of the well-known Correlation Clustering, where we are additionally given the set of friendly pairs and the set of hostile pairs and the goal is to find an optima... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and comments.
First we reply to their two main concerns, that is, the practicality of our algorithm and Lemma 4.4.
---
Regarding the practicality of our algorithm, as the reviewer noted, the bottleneck is in solving the Constrained Cluster LP. We... | Summary: This paper proves a better-than-2 approximation for constrained correlation clustering (correlation clustering where certain "friendly" pairs are required to be in the same cluster and other "hostile" pairs are required to be separated). The approach combines two recent techniques for standard correlation clus... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions, we will apply them all in the final version.
Regarding the proof-by-contradiction: Indeed, this claim of ours is inaccurate; this is how it was treated in the original local search paper [Combinatorial Correlation Clustering; Cohen-Addad et al.; STOC 2... | Summary: The main contribution of this paper is the development of a 1.94-approximation algorithm for the constrained correlation clustering problem. In this context, the input is a graph consisting of edges labeled as {+1, -1}. The objective of correlation clustering is to find a partition (clustering) of the nodes th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their suggestions, we will apply them all in the final version.
In particular, regarding APX-Hardness of Correlation Clustering:
- It was shown that Correlation Clustering is APX-Hard, but without an explicit constant, in [Clustering with qualitative information; Charika... | Summary: The authors consider the classic Correlation Clustering problem which, given a complete graph with edges labeled either + or -, the goal is to find a partition of the vertices so as to minimize the number of + edges across parts plus the number of - edges within parts. The has received a lot of attention since... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestion. We inherited the "competitive" terminology from the local search paper [Combinatorial Correlation Clustering; Cohen-Addad et al.; STOC 2024], but we understand now that mixing the two can be confusing. We will stick to "approximate". | null | null | null | null | null | null |
Stochastic Encodings for Active Feature Acquisition | Accept (poster) | Summary: Considering the training challenges of reinforcement-learning approaches and the myopic shortcomings of conditional mutual information (CMI) strategies, this paper proposed a method called Stochastic Encodings for Active Feature Acquisition (SEFA). By encoding features into a regularized, stochastic latent spa... | Rebuttal 1:
Rebuttal: Thank you for the positive review, we are grateful for the feedback, we answer your questions below. All changes below will be added.
# Theory
We agree that the theory provided is specific to the indicator and does not provide general statements about bounds on SEFA's performance. The purpose was... | Summary: This paper addresses Active Feature Acquisition (AFA), the task of sequentially selecting which features to measure for a specific test instance to improve prediction accuracy while minimizing the number of features acquired. The authors identify limitations in existing approaches: Reinforcement Learning (RL) ... | Rebuttal 1:
Rebuttal: Thank you for the positive review, we are grateful for the feedback, we answer your questions below. All changes below will be added.
# Scalability
We have addressed this shared point in our response to Reviewer ABGZ.
# Generalization to Regression
SEFA can be modified for regression so that the... | Summary: This paper considers active feature acquisition, which is a dynamic test-time selection (usually sequential) of features to make predictions on each test instances. The selected features are chosen independently for each considered test instance. The authors argue (with some theory) how prior methods based on ... | Rebuttal 1:
Rebuttal: Thank you for the positive review, we are grateful for the feedback, we answer your questions below. All changes below will be added.
# Renyi Entropy
Thank you for bringing this to our attention, we agree that Renyi entropies are relevant. In particular how the min-entropy relates to Shannon Entr... | Summary: The paper proposes a Stochastic Encodings for Feature Acquisition (SEFA) framework addressing the limitations of CMI and RL methods. SEFA uses an encoder-predictor architecture with intermediate stochastic latent variables. The architecture makes predictions and calculates the acquisition objective.
Claims An... | Rebuttal 1:
Rebuttal: Thank you for the positive review, we are grateful for the feedback, we answer your questions below. All changes below will be added.
# Scalability
We provide the computational complexities for a single training step and single acquisition step for each model in Table 4 (Appendix H.3). The main t... | null | null | null | null | null | null |
Concurrent Reinforcement Learning with Aggregated States via Randomized Least Squares Value Iteration | Accept (poster) | Summary: The authors extend the classic Least Squares Value Iteration (LSVI) method to the setting of multi-agent systems, and show guarantees for a randomized variant.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes, to some extent.
Experimental Designs Or Analyses: Yes, to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive feedback!
1. We will add an impact statement later :)
2. [2] improves the regret bound of [1] by using clipping in their algorithm to avoid unreasonable estimates of the value functions. The rest of their algorithm closely follows [1], using a similar pro... | Summary: The paper presents a novel approach to concurrent reinforcement learning (RL) using Randomized Least Squares Value Iteration (RLSVI) with aggregated states. The authors propose a framework where multiple agents interact with a common environment, sharing their experiences to improve decision-making collectivel... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive comments.
1. We would like to emphasize two points regarding the role of experiments in this work. First, the primary contribution of this paper is theoretical analysis of concurrent model-free RLSVI algorithms. Notably, neither o... | Summary: The paper adapts the concurrent learning framework via randomized least squares value iteration with an aggregated state representation, to improve exploration efficiency and the worst-case regret bound. Extensive experiments are conducted to support the theoretical findings.
Claims And Evidence: The claims a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and constructive comments. We are aware that our experiments are only on synthetic data. We would like to emphasize two points regarding the role of experiments in this work.
Firstly, the main focus and contribution of our work is the first theore... | null | null | null | null | null | null | null | null |
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator | Accept (poster) | Summary: This paper proposes SepLLM, an efficient Transformer-based architecture designed to accelerate inference in large language models (LLMs). The key insight is that separator tokens (such as punctuation and line breaks) disproportionately carry segment-level information, enabling the compression of other tokens w... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for the valuable comments on our work. We take every comment seriously and hope our response can address the reviewer’s concerns. If there are any remaining questions, we are more than happy to address them.
> Q1. The authors do not clearly explain or... | Summary: ## update after rebuttal
The author's rebuttal addressed many of my concerns; however, I am still a little hesitant about the generalisability. I will change my score from 2 to 3.
* The paper identifies a key pattern: certain seemingly meaningless special tokens (i.e., separators) contribute disproportionate... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for the valuable comments on our work. We take every comment seriously and hope our response can address the reviewer’s concerns. If there are any remaining questions, we are more than happy to address them.
**Should there be any need for further clar... | Summary: The paper introduces SepLLM, a novel framework aimed at improving the efficiency of large language models (LLMs) by leveraging the observation that separator tokens (e.g., commas, periods) disproportionately contribute to attention scores. The authors propose compressing segment information into these separato... | Rebuttal 1:
Rebuttal: We would like to sincerely thank the reviewer for the valuable comments on our work. We take every comment seriously and hope our response can address the reviewer’s concerns. If there are any remaining questions, we are more than happy to address them.
**Should there be any need for further clar... | null | null | null | null | null | null | null | null |
Projection Pursuit Density Ratio Estimation | Accept (poster) | Summary: This paper considers the problem of density ratio estimation (DRE), where parametric methods may leads to bias, while non-parametric methods struggle with the curse of dimensionality in high-dimensional settings. The authors present an approach that leverages projection pursuit approximation to estimate the de... | Rebuttal 1:
Rebuttal: Thank you for the positive comments.
*Q1: Theoretical support for the superiority of the proposed method*
We develop asymptotic convergence rates (Theorem 3.2) for our proposed projection pursuit density ratio estimator:
$$ \sup_{x\in\mathcal{X}}| \hat{r}\_{K}(x) - r_{K}(x)| =O_{p}(\sum_{\ell=1}... | Summary: This paper proposes a non-parametric method for the density ratio estimation (DRE) task using a projection pursuit (PP) approximation. The method is computationally convenient and also helps alleviate the curse of dimensionality. The authors conduct extensive experiments demonstrating that the proposed method ... | Rebuttal 1:
Rebuttal: Thanks for your suggestions.
*Q1: Computational cost evaluation in high-dimensional cases*
Compared to the conventional projection pursuit method (kindly refer to our response to Q1 of Reviewer fbc2) that requires additional Monte Carlo sampling to estimate the pursuit function, our estimators o... | Summary: This paper introduces a novel projection pursuit (PP)-based method for density ratio estimation (DRE), a critical task in machine learning with applications in areas like causal inference and covariate shift adaptation. Addressing the limitations of parametric methods (potential bias) and non-parametric method... | Rebuttal 1:
Rebuttal: Thank you for the constructive comments.
*Q1: Error bound for density ratio $\hat{r}$*
The main result in the original manuscript gives the convergence rates of $\hat{f}\_{a,k}$ and $a_l$ given the estimate $\hat{r}\_{k-1}$. Since our estimator $\hat{r}\_k$ is computed iteratively, and $\hat{r}\... | Summary: Parametric methods for DRE are susceptible to bias when model assumptions are misspecified, whereas traditional non-parametric approaches often struggle with the curse of dimensionality in high-dimensional settings. To overcome these limitations, the authors suggest using a projection pursuit (PP) based approa... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments. Below, we address them point by point.
*Q1: Comparison with projection pursuit density estimation*
[1] and [2] focused on estimating a pdf $p(x)$. In contrast, we aim at estimating the ratio $r^*(x)=p(x)/q(x)$ between two probabilities densities, which is s... | null | null | null | null | null | null |
Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint | Accept (poster) | Summary: The manuscript presents an advanced study on subset selection problems, which are prevalent in various fields such as machine learning, operations research, and economics. The authors focus on subset selection under a linear cost constraint, a problem characterized by its NP-hardness and practical importance. ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback and for recognizing the strengths of our work. We appreciate your kind words regarding our contributions and writing. Please find our detailed responses below.
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> I understand that this work is mainly a theoretical contribution, but if we can com... | Summary: This paper contributed to 1) analyzing the existing approach for submodular optimization with a linear inequality constraint, and 2) proposing a novel approach with a better approximation guarantee and better practical performance. This paper first analyzed the existing approach, POMC, an evolutionary algorith... | Rebuttal 1:
Rebuttal: We sincerely appreciate your positive feedback and encouraging validation of our work's strengths. Please find our detailed responses below.
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> The discussion about the potential real-world applications would improve the visibility of this paper.
The discussion about the potential real-world... | Summary: This paper studies the problem of subset selection with a linear cost constraint. The authors first improved the approximation guarantee (from (1-1/e)/2 to 1/2) of an existing evolutionary algorithm, so-called POMC, with the best empirical performance. Then, they proposed a new evolutionary algorithm EPOL with... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable suggestions. We sincerely appreciate your feedback, which will help us improve our manuscript. Below are our detailed responses to your queries.
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> ... more explanations. Why the formulas in cases (1)-(3) satisfy Equation (6) with $i +c(v^*)$?
... | Summary: This paper addresses monotone submodular maximization under a linear cost constraint using evolutionary algorithms. It reanalyzes the Pareto Optimization Algorithm for Monotone Cost functions (POMC), providing an improved approximation ratio of $1/2$. Additionally, the authors propose a novel multi-objective e... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We greatly appreciate your time and thoughtful feedback. Below are our detailed responses.
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> The core idea behind improving the approximation ratio of POMC should be introduced at a high level at the beginning of Section 3 ...
> Moreover, the algorithm se... | null | null | null | null | null | null |
Wide & Deep Learning for Node Classification | Reject | Summary: The paper identifies the _over-generalization_ problem in the GCNII model and addresses it by proposing GCNIII, which leverages a Wide & Deep architecture. The effectiveness of this approach is validated through experiments.
Claims And Evidence: Claims are not clear, see **Other Strengths and Weaknesses**.
M... | Rebuttal 1:
Rebuttal: Thank you very much for your review, suggestions, and questions. We have carefully addressed your concerns shown below.
Q1: Conducting a separate hyperparameter search under your method's framework would be a fairer comparison.
A1: Thanks for your suggestion. We re-optimized all hyperparameters,... | Summary: This paper improves the architecture of the existing model, GCNII, mainly based on the idea "wide & deep". Also, LLM is used to encode the node features.
Claims And Evidence: 1. The authors claimed on page 5 that "The former is 0.0018, while the latter is 0.8423, which demonstrates that GCNII’s “attention” ca... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
Q1: The authors claimed on page 5 that "The former is 0.0018, while the latter is 0.8423, which demonstrates that GCNII’s “attention” captures more information, leading to stronger generalization." However, it is not clear what i... | Summary: This paper proposes a new model GCNIII which aims to get more effectively balance the trade-off between over-fitting and over-generalization. The framework incorporates three key techniques: intersect memory, initial residual and identity mapping. Experiments conducted on benchmark datasets demonstrate the e... | Rebuttal 1:
Rebuttal: Thanks a lot for your review, suggestions, and questions. We have carefully addressed your concerns shown below.
Q1: The proposed techniques demonstrate limited innovation in their conceptual design. Specifically, the intersect memory mechanism appears overly simplistic in its implementation. Fur... | Summary: This work proposes a new gnn architecture which uses the wide & deep neural network framework to address the problems of over-fitting and over-generalization occurring the current deep graph neural networks, which combines the linear model for initial node features and the deep graph convolution layers. In p... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
Q1: There is lack of comparison between the training error and the validation error (like figure 1) for the proposed method, only training losses are reported in Figure 5 for three different models.
A1: Regarding Figure 5: To s... | null | null | null | null | null | null |
Supercharging Graph Transformers with Advective Diffusion | Accept (poster) | Summary: The paper analysis the generalization capabilities of a specific class of diffusion-based graph models, namely those following the advective diffusion transformer, under topology distribution shifts. Specifically, it highlights that while the generalization gap for local diffusion—relying solely on the adjacen... | Rebuttal 1:
Rebuttal: Thank you for your time and thoughtful feedback.
**Q1: Concerns about theoretical assumptions and (3.5) of (Van Loan, 1977)**
We appreciate the opportunity to clarify this point. The reviewer is correct that the result (3.5) of (Van Loan, 1977) [1] requires the matrix to commute with its transpo... | Summary: This paper proposes a framework called Advective Diffusion Transformer (ADIT) to address topological distribution shifts in graph-structured data. By formulating a continuous diffusion equation augmented with an advection term, the authors effectively combine non-local propagation with local message passing. T... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback.
**Q1: Theoretical assumptions and justification on more relaxed real-world conditions**
Our PDE model indeed serves as a simplified abstraction, but it does not introduce new assumptions beyond the standard setting of graph represen... | Summary: This paper proposes advective diffusion transformer, a model that provably controls generalization error with topological shifts. The model has been evaluated on synthetic and several real-world datasets that verify its superiority compared with existing local and non-local graph neural networks/transformers.
... | Rebuttal 1:
Rebuttal: Thank you for your time in reviewing our paper and for the constructive feedback.
**Q1: "How is the performance of the proposed approach on the OGB benchmarks?"**
We appreciate the suggestion to include additional datasets. We added results on OGBL-COLLAB, a link prediction task suited for evalu... | null | null | null | null | null | null | null | null |
Spectral Informed Neural Networks | Reject | Summary: This paper proposes spectral-informed neural networks (SINNs), which solve PDEs using spectral information. SINNs uses less memory than PINNs, especially for problems with higher-order derivatives, and it also obtains better accuracy than PINNs across several experimental settings. The authors also provide a t... | Rebuttal 1:
Rebuttal: Thank you for your extensive feedback. We understand that you have several questions about the paper, and we address each of your points in turn below.
# Questions 1 and Other Comments Or Suggestions
First and foremost, we apologize for the multiple typos and notational inconsistencies you encoun... | Summary: The Manuscript describes how to use spatial k-space description of the trial neural network with promising results. The general form of the PDE is assumed to be first order in time with periodic boundary conditions in space. Numerous essential PDEs are considered and it is shown that the naturally sparse k-sp... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and useful suggestions.
Firstly, we sincerely appreciate your having caught this typo of $\nabla$ which should be $\nabla$ $\cdot$.
# Burgers’ equation with low viscosity
We already conducted an experiment on Burgers’ equation with a fairly low viscosity ($\... | Summary: PINNs have arisen as an exciting and promising alternative to classical solution methods for solving partial differential equations.
However, PINNs are not without their challenges. One key issue is the cost of automatic differentiation for higher-order derivative PDEs. It is well-known that the cost scales w... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address each of your points below:
# Is there any particular reason why you did not compare with STDE?
STDE is indeed a recent work to speed up high-order derivatives in PINNs. However, STDE works by amortizing the cost of computing mixed partials in multiva... | Summary: This paper introduces Spectral-Informed Neural Networks (SINNs) as an alternative to standard Physics-Informed Neural Networks (PINNs) for solving PDEs. Instead of computing spatial derivatives through automatic differentiation, SINNs leverage spectral methods, replacing differentiation with simple multiplicat... | Rebuttal 1:
Rebuttal: Thanks for the positive assessment and thoughtful questions.
# Challenging Problems
Firstly, we consider a well-defined 1D Helmholtz equation as you suggested:
$$
u_{xx} + \lambda^2u=0, x\in [0,2\pi],
$$
with the boundary condition
$$
u(0)=u(2\pi)=1, \quad u_x(0)=u_x(2\pi)=\lambda.
$$
The soluti... | null | null | null | null | null | null |
Do NOT Think That Much for 2+3=? On the Overthinking of Long Reasoning Models | Accept (poster) | Summary: The authors observe that these models often allocate excessive computational resources to simple problems, leading to inefficiencies. To address this, they introduce novel efficiency metrics from both outcome and process perspectives and propose self-training strategies to streamline reasoning without compromi... | Rebuttal 1:
Rebuttal: Thank you for your questions! We will answer the questions mentioned in your review.
Q: Do you have the results for the proposed efficiency enhancing methods with more models?
A: Yes, we have conducted experiments on the R1-Distilled-Qwen-32B model, and our efficiency-enhancing methods remain ef... | Summary: This paper addresses the "overthinking" issue observed in o1-like reasoning models, which expend excessive computational resources during inference, especially for simple problems. The authors first analyze this phenomenon by prompting LLMs with trivial questions (e.g., "What is the answer of 2 plus 3?"), obse... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We address each question raised in your review individually below.
Q (Weakness): While the metrics and simplification methods are effective, the generalizability to domains beyond mathematical reasoning tasks remains unclear.
A: This is a valuable point. ... | Summary: This paper studies the over-thinking problem of o1-like models. The overthinking refers to the scenario where unnecessary compute resource is used for simple question.
It first compares the average tokens needed by traditional LLM and the o1-like reasoning models. It finds that o1-like models spend much more ... | Rebuttal 1:
Rebuttal: Thank you very much for your questions. Below we address each point mentioned in your review.
Since most of your questions pertain to Section 3, we provide additional clarifications on our methods as follows:
1. Our proposed methods focus primarily on constructing suitable training data. Specifi... | Summary: This paper investigates the phenomenon of "overthinking" in recent "o1-like" large language models (LLMs), where models expend excessive computational resources (generating many tokens and solution steps) on simple problems with minimal benefit to accuracy or reasoning diversity. The authors provide the first ... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful questions and suggestions.
Q1: Provide the code for reproduction.
A1: We will make our code publicly available soon.
---
Q2: The term "o1-like models" isn't precisely defined.
A2: Thank you for pointing this out. We acknowledge that our original descript... | null | null | null | null | null | null |
Wasserstein Flow Matching: Generative Modeling Over Families of Distributions | Accept (poster) | Summary: The paper shows how Riemannian Flow Matching (RFM) (Chen & Lipman, 2023) is applied to the Wasserstein metric space, the space of distributions endowed with the Wasserstein metric. For RFM to work, a parametric vector field that such that when marginalizing it over the [0,1] time interval, turns a source, Gaus... | Rebuttal 1:
Rebuttal: # Response to Reviewer 4 (**euNm**)
We thank the reviewer for their insightful feedback and positive assessment of our work. We address each point below.
**"...What I have seen in the paper is that every point cloud in consideration are sampled with 1,000 points. This is too small to represent a... | Summary: The paper addresses the problem of learning generative models of high-dimension distribution, i.e., where each sample from the model is a distribution itself. The authors propose Wasserstein flow matching (WFM), which builds on top of recent advances in the Riemannian flow matching (RFM) framework (Chen & Lip... | Rebuttal 1:
Rebuttal: # Response to Reviewer 3 (**ToSj**)
We are grateful to the reviewer for their positive assessment of our work.
**"While Tab 3 reveals that WFM performs similarly to other methods on ShapeNet and ModelNet, it never outperforms them."**
All reviewers expressed concerns about our performance in 3D... | Summary: This paper proposes Wasserstein Flow Matching, a method for building a generative model where the datapoints themselves are distributions. The basic idea is to treat the Wasserstein space as a manifold and use Riemannian flow matching techniques. The model is studied in two settings: (1) the Bures-Wasserstein ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 2 (**C2Yo**)
Thank you for your review and insightful comments. We address each of your concerns below:
**"The authors claim that existing approaches for 3D point cloud generation cannot handle point clouds of variable sizes. However, a method like PSF for example should i... | Summary: This paper proposes Wasserstein flow matching, a generative model which can be treated a variant of Riemannian Flow Matching on Wasserstein manifold. This model allows for working with the families of distributions and is tested in a variety of experiments.
## **After the rebuttal.**
I kept my initial positi... | Rebuttal 1:
Rebuttal: # Response to Reviewer 1 (**8NGf**)
Thank you for your thoughtful review and constructive feedback.
**"Overall, the proposed method and evaluation criteria make sense. However, the paper is lacking an experiment which shows that the proposed approach beats its competitors in the case of non-Gaus... | null | null | null | null | null | null |
Risk-Sensitive Theory of Mind: Coordinating with Agents of Unknown Bias using Cumulative Prospect Theory | Accept (poster) | Summary: This paper presents a new method for learning agent strategies that support human decision-making under risk. The agent has the ability to learn strategies in situations where it does not know how risk-averse its partner is. The paper adopts prospect theory as a model for human decision-making and proposes a f... | Rebuttal 1:
Rebuttal: Thank you for your comments and feedback. We have addressed them below.
> **C1:** The reviewer asked “(Kwon et al., 2020; Sun et al., 2019; Danis et al., 2023; Ferreira et al., 2021) What is the reason why these methods cannot be applied? Even if they cannot be applied straightforwardly, I would ... | Summary: This paper proposes a multi-agent RL method where agents model the other agents under the cumulative prospect theory (CPT) model. This allows better coordination with risk-averse or risk-seeking agents (an agent may have both tendencies depending on the context), which includes human agents.
The proposed meth... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
> **C1:** The reviewer is concerned that “the claim is to better model humans' decisions under CPT-related biases which will then lead to better collaboration, there is no evidence about it in the paper due to the lack of human experiments”
We would lik... | Summary: This paper proposes a new model of risk-sensitive multi-agent coordination towards better aligning autonomous agents with human utilities. The authors define a risk-sensitive ToM that affords adaptation to a partner with unknown risk-sensitivity in in a zero-shot
fashion by pre-training several policies with d... | Rebuttal 1:
Rebuttal: Thank you for your comments.
> **C1:** The reviewer commented on the generalization of our algorithm beyond these toy scenarios and that alternative models may give rise to the same behavior. They suggest that we mention this in limitations.
We generally agree that the observed behaviors could ... | Summary: The authors consider the problem of a two-agent cooperative sequential decision task, in which one of the agents is a human, and address the problem of learning to coordinate behavior in the second agent. The idea is to model human behavior as following cumulative prospect theory, i.e. scaling probabilities an... | Rebuttal 1:
Rebuttal: We appreciate your effort in providing comments and respond to them below.
> **C1:** The reviewer asked why we did not include a limited human study.
We recommend you check the response to Reviewer 3GLy’s C1 for a full description of the summary we provide here.
While “...capturing human behav... | null | null | null | null | null | null |
BackSlash: Rate Constrained Optimized Training of Large Language Models | Accept (poster) | Summary: This work proposes Rate-Constrained Training (RCT), a novel training method that allows for training LLMs in a way that allows for effective compression of their weights at the end of training. The main idea is to derive a weight regularizer by assuming a specific distribution over the model weights, the gener... | Rebuttal 1:
Rebuttal: **Comment #1: Evaluation against the Gaussian model and the Laplace model.**
**Response:** We thank the reviewer for valuable suggestion. We have conducted experiments with L0.5, L1, L2 regulations, the latter two correspond to the Gussian and Laplacian models. As can be seen, RCT with EG achieve... | Summary: The paper introduces Rate-Constrained Training (RCT), which can integrate compression during the training phase using rate-distortion optimization. The authors observed that LLM parameters follow a generalized Gaussian distribution (GG) with shape parameters less than 2. Thus, the proposed idea is to use rate... | Rebuttal 1:
Rebuttal: **Comment #1: Analysis of "EG codes is robust with regard to parameter mismatches."**
**Response:** We are sincerely grateful to the reviewer for providing such valuable advice. Table. 3 demonstrates 0-order EG's optimality across models with varying shapes, confirming its robustness. Theoretical... | Summary: The paper presented Rate-Constrained Training (RCT) for Large Language Models (LLMs), exploring model compression in the training stage. The paper showed that parameters of representative LLMs typically followed generalized Gaussian instead of vanilla Gaussian. The paper further enforced the distribution const... | Rebuttal 1:
Rebuttal: **Comment #1: Baseline: include baselines like, vanilla training + vanilla Huffman coding, etc.**
**Response:** We greatly appreciate the suggestion, and will incorporate Huffman coding as a baseline. We found that although Huffman coding shows a small advantage in efficiency over EG when applied... | Summary: The paper introduce Rate-Constrained Training (RCT), a method integrating rate-distortion optimization into the training process of Large Language Models (LLMs). RCT leverages a generalized Gaussian (GG) distribution to accurately model LLM parameters and uses exp-Golomb (EG) coding for entropy-efficient param... | Rebuttal 1:
Rebuttal: **Comment #1: The optimal selection of the Lagrange multiplier (λ) is pretty empirical, there is no theoretical guidance or any adaptive strategies to systematically select λ across different tasks.**
**Response:** We thank the reviewer the valuable feedback. Indeed the theoretical foundation for... | null | null | null | null | null | null |
Exact risk curves of signSGD in High-Dimensions: quantifying preconditioning and noise-compression effects | Accept (poster) | Summary: The paper studies the precise risk curves of signSGD in high dimensional limit for quadratic loss with Gaussian data under certain assumptions on the label noise. It contrasts the risk curves with SGD and quantifies the differences in terms of four effects - effective learning rate, noise compression, precondi... | Rebuttal 1:
Rebuttal: Thank you for the review, we’re glad that you appreciate our contributions. Our expectation that $K_\sigma$ inherits a power-law spectrum from $K$ is mostly speculative. We should qualify that this claim is basis dependent. If $K$ is diagonal then $K_\sigma$ is the identity, which collapses any po... | Summary: This paper studies SignSGD, which can be viewed as Adam without the moment accumulator, in the high-dimensional limit. The main goals of the paper are to quantitatively understand the observed preconditioning and "noise compression" effects of SignSGD in practice. Toward this end, a limiting SDE (SignHSGD) and... | Rebuttal 1:
Rebuttal: Thanks for your comments, we address some key questions here.
**NTK Regime**
If we write consider the mean square loss for a general neural network $f(\theta, x)$ in the NTK regime such that $f(\theta, x) = f(\theta_0, x) + \nabla_{\theta} f(\theta_0, x) ^T (\theta_k - \theta_*)$ then the risk, ... | Summary: The paper studies the dynamics of signSGD in a linear regression setting with Gaussian covariates $x \sim N(0,K)$ and noisy labels $y = \langle x,\theta_\ast \rangle + \epsilon$. The authors derive a limiting SDE (signHSGD) that describes the dynamics of $\theta$ as the dimension $d \to \infty$ which depends o... | Rebuttal 1:
Rebuttal: We thank the author for their detailed review and address weaknesses and questions below.
## Weaknesses
**Noise reshaping**
For the $K \to K_\sigma$ mapping, there is strong dependence on the eigenbasis of $K$. For example if $K$ is diagonal then $K_\sigma = Id$. Otherwise, if the eigenbasis i... | Summary: The authors analyze Sign-SGD for linear regression in high dimensions by deriving limiting differential equations that describe its behavior. Their analysis quantifies four main effects: learning rate adjustment, noise compression, diagonal preconditioning, and gradient noise reshaping. The paper includes theo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and address some key points below.
## Minbatching:
We have added more details regarding batch sizes $b$ in our response to Reviewer ugE8. One particular consequence of minibatching is that our condition on the behaviour of the noise near $0$ relaxes sign... | null | null | null | null | null | null |
Pointwise Information Measures as Confidence Estimators in Deep Neural Networks: A Comparative Study | Accept (poster) | Summary: This paper presents a comparative analysis of three point-wise information measures—PMI, PVI, and PSI—from both theoretical and empirical perspectives. The study theoretically analyzes the sensitivity of these measures to margin effects and intrinsic dimensionality, along with their convergence rates. Empirica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive remarks and valuable suggestions on the paper. Below, we address the concerns and questions raised by the reviewer.
**Weakness 1 (On the Assumption for PVI's Theoretical Properties)**
This is a valid point, and we will explicitly specify the limitations o... | Summary: The authors propose a new indicator for quantifying the confidence of a neural network: the pointwise information between an input’s features and the corresponding output. When the information is high (corresponding to a large reduction in the entropy of the true label when conditioned on the input), we expect... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive remarks and valuable suggestions on the paper. Below, we address the major concerns and questions raised by the reviewer. We will address all other points in the revision.
**Question 1 (On Invariance)**
We would like to clarify that we meant bijective lin... | Summary: This paper proposes to use three information-theoretic measures—Pointwise Mutual Information, Pointwise V-Information, and Pointwise Sliced Mutual Information, as post-hoc confidence estimators for neural network predictions. It theoretically analyzes their invariance properties, sensitivity to geometric featu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive remarks and valuable suggestions on the paper. Below, we address the concerns and questions raised by the reviewer.
> Empirical evaluation lacks comparison to the aforementioned lines of work.
The aforementioned lines of work are not post-hoc calibration ... | Summary: The paper explores the use of information-theoretic measures—specifically pointwise mutual information (PMI), pointwise V-information (PVI), and pointwise sliced mutual information (PSI)—to estimate prediction confidence in deep neural networks (DNNs) post-hoc, without modifying network architecture or trainin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive remarks and valuable suggestions on the paper. Below, we address the concerns and questions raised by the reviewer.
**Weakness 1 (On OoD Experiment)**
The primary motivation of our work stems from the observation that existing calibration methods can adve... | null | null | null | null | null | null |
Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate | Accept (poster) | Summary: This paper introduces a novel method for de novo enzyme design using retrieval augmentation for the generative process. The core of this method is to take a given substrate for the enzyme, search in an enzyme database for protein sequences that are enzymes of similar substrates, align these protein sequences t... | Rebuttal 1:
Rebuttal: Dear Area Chair and Reviewers,
We sincerely thank you for your thoughtful and thorough evaluation of our paper.
# `UniKP for evaluation`
Thanks for your comment. Using the predictive model for scoring is what we do in practice to filter better proteins. Before the wet lab experiment, proteins a... | Summary: The paper introduces SENZ, a substrate-specified enzyme generator, a RAG-based method to retrieve known enzymes and generate new enzymes based on a substrate. The authors define the task as generating a protein that serves as an enzyme for a given small molecule target. As a first step, the authors design a re... | Rebuttal 1:
Rebuttal: Dear Area Chair and Reviewers,
We sincerely thank you for your thoughtful evaluation.
# `Retrieval quality`
The quality of retrieved enzymes is reflected in the baseline method named '**Retrieved**' in Table 2. That row is as follows:
| Method | $k_{cat}$ | pLDDT |
| --------- | --------- |... | Summary: This paper presents a retrieval-augmented approach for enzyme sequence generation, conditioned on substrates. The authors introduce a novel benchmark designed to address the zero-shot setting, which is aligned with downstream generation tasks. The proposed method utilizes a diffusion model to iteratively gener... | Rebuttal 1:
Rebuttal: Dear Area Chair and Reviewers,
We sincerely thank you for your thoughtful and thorough evaluation of our paper.
# `Mentioned reference`
We will include these six pieces of literature in the related work section in the final version of this paper since modification to the manuscript is not allow... | Summary: This manuscript proposes the SENZ method for zero-shot substrate-specified enzyme generation. Its key points include: (1) defining the task and constructing a substrate-enzyme dataset; (2) retrieving relevant enzymes based on substrate similarity; (3) generating new enzymes via a diffusion model guided by a cl... | Rebuttal 1:
Rebuttal: Dear Area Chair and Reviewers,
We sincerely thank you for your thoughtful and thorough evaluation of our paper. Below are our detailed responses to your comments:
## **`Source of the 7 substrates used in the experiments and reason of selection`**
Thanks for your comment. We selected these subst... | null | null | null | null | null | null |
MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning | Accept (poster) | Summary: The paper proposes is an effective framework for enhancing many-shot ICL performance in scenarios with limited labeled data. B selecting unlabeled samples for pseudo-labeling based on their influence on labeled data and by adaptively selecting demonstrations tailored to each query, the method significantly re... | Rebuttal 1:
Rebuttal: >**W1.** Practicality Concerns.
>
**Response**: Thank you for raising concerns regarding computational complexity. To address practicality, our framework incorporates strategies to improve efficiency significantly:
By employing a KV cache, we reduce computational costs by fixing labeled and pseud... | Summary: This paper presents MAPLE, a method for pseudo-labeling in many-shot ICL settings. Key innovation includes similarity-based selection for pseudo-labeling and demonstration example selection.
Claims And Evidence: 1. It’s interesting to study many-shot ICL under pseudo-label settings, which has practical value.... | Rebuttal 1:
Rebuttal: >**Claim** The author claims “strong performance” for the MAPLE, but didn’t specify the baselines nor quantitative results for comparison.
>
**Response**: In our experiments, MAPLE is primarily compared quantitatively against 5 baseline methods on 8 datasets with 5 settings (i.e., the number of ps... | Summary: The paper considers semi-supervised many-shot in-context learning setting, i.e., having small labeled and large unlabeled support sets to perform in-context learning with long-context LLMs. Authors argue that within this problem setting it would be beneficial to (i) identify the most impactful unlabeled sample... | Rebuttal 1:
Rebuttal: >**Q1.** Do you really need to cut Top-K edges for each node? Is it correct that Top-K only controls speed and does not affect the performance, or there is some interplay between both Top-K and Top-P that affect the performance?
>
**Response**: Thank you for the question. We want to clarify that ... | Summary: This work develops a semi-supervised in-context learning framework by exploiting small amount of labeled and large unlabelled dataset. A Ken graph is built upon the labeled and unlabelled dataset. The unlabelled samples (nodes) that are similar to the labeled ones are selected
For pseudo labelling. Finally, d... | Rebuttal 1:
Rebuttal: >**W1.** Building a graph for selecting relevant unlabelled samples itself induces additional computation overhead. There is no discussions on the computation cost for graph construction.
>
**Response**: Thank you for bringing up this point. The graph construction requires the computation of the r... | null | null | null | null | null | null |
Impossible Videos | Accept (poster) | Summary: The paper introduces IPV-VID, a dataset designed to evaluate video understanding models on "impossible videos", which depict scenarios that violate commonsense. The study evaluates from two perspectives: video understanding and video generation. For video understanding, benchmark tasks such as VideoQA and vide... | Rebuttal 1:
Rebuttal: Thanks for the encouraging review and valuable suggestions. We appreciate your acknowledgment of the paper’s novelty and will carefully address your concerns to improve clarity and rigor.
**Q1: Disentangling Impossibility vs. Synthetic Data Quality**
Disentangling impossibility from synthetic da... | Summary: This paper introduces the novel concept of "impossible videos" as a challenging testbed for advancing video understanding and generation models. It proposes the IPV-Text benchmark composing of many anti-reality videos for evaluating the video LLM models in the understanding task. Results in the paper reveal th... | Rebuttal 1:
Rebuttal: Thank you for your encouraging review and valuable suggestions. We appreciate your acknowledgment of the paper’s novelty and will carefully address your concerns to improve clarity and rigor. Below are our detailed responses:
**Q1: Better to explain more clearly about how to calculate the score i... | Summary: The paper introduces IPV-BENCH, a novel benchmark designed to evaluate video understanding and generation models from the perspective of impossible videos. It categorizes scenarios that violate physical, biological, geographical, and social laws. The main experimental results reveal that current models have di... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and constructive suggestions. We are grateful for your recognition of our work’s novelty and will incorporate your recommendations to further strengthen the paper. Below, we address your comments in detail.
**Q1: Claims about model limitations of understanding... | Summary: The paper introduces IPV-BENCH, a benchmark for evaluating video understanding and generation models using "impossible videos". It includes a taxonomy, a prompt suite (IPV-TXT), and a video dataset (IPV-VID). Evaluations reveal limitations in current models, highlighting the need for improved reasoning and gen... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We greatly appreciate your insights on our paper. Below, we outline our responses to each point.
**Q1: Reproducibility: Code/Dataset Release**
We appreciate the reviewer’s concern regarding reproducibility. Upon acceptance, we will publicly release all **c... | null | null | null | null | null | null |
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees | Accept (poster) | Summary: In this paper, the authors study a personalized FL objective and showed its statistical accuracy under strongly convex, smooth model. The authors then propose a new algorithm to solve the problem. Empirical results show that as the personalization level changes, the model is able to interpolate between pure lo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive comments. Below, we provide our detailed responses to each point. We hope these clarifications help address your concerns.
**Comparison with Ditto [1]**
Our work is significantly different from Ditto (Li et al. 2021 [1]) in multiple aspe... | Summary: This paper studies the trade-off of accuracy and communication in personalized federated learning and presents the theoretical analysis of the effects of personalization degree. The theoretical findings are validated on synthetic and real-world datasets.
Claims And Evidence: The claims are supported by theore... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive comments. Below, we provide our detailed responses to each point. We hope these clarifications help address your concerns.
**The practical implication on the choice of $\lambda$**
For the practical implication of personalization in our ex... | Summary: The authors theoretically address the accuracy-communication trade-off in personalized federated learning (FL). In other words, $\lambda$, which controls the regularization between global and local models, represents the accuracy-communication trade-off, and an analysis of this is conducted.
Claims And Eviden... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive comments. Below, we provide our detailed responses to each point. We hope these clarifications help address your concerns.
**The summarized key takeaway as our main claim**
From a statistical perspective (Section 4.1), we provide a tight... | Summary: This paper studies personalized federated learning, i.e., where data owners (clients) have their own distribution.
The paper studies in particular the trade off between exploiting more shared knowledge (increasing the communication cost) and relying mire on local data.
Claims And Evidence: The theorems are su... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive comments. Below, we provide our detailed responses to each point.
**The Structure of the Theoretical Proof**
We establish the statistical convergence rate (Theorem 1) in Section A.3. Specifically, Section A.3.1 derives the statistical ra... | Summary: This paper proposed a personalized federated learning algorithm that captures the relationship between communication cost and the degree of personalization. Convergence theories are derived, showing that the total required gradient steps are irrelevant to the personalization degree $\lambda$, while the scaling... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive comments. Below, we provide our detailed responses to each point. We hope these clarifications help address your concerns.
**The concern raises for the definition of parameter space**
A natural measure of local model's heterogeneity could... | null | null | null | null |
Mirror, Mirror of the Flow: How Does Regularization Shape Implicit Bias? | Accept (poster) | Summary: The authors propose to analyze the effect of combining implicit and explicit regularization on training dynamics using the mirror flow framework. While their framework is more general, they specifically examine the impact of weight decay when it is turned off at a particular training step. To investigate this,... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and valuable feedback. We’re happy to address any further questions or concerns.
**Turning-off at different points**
We are happy to provide an ablation on turning off the regularization at different points for the vision transformers:
We disable weight decay (W... | Summary: Motivated by the fact that the inductive bias of a trained neural network depends on implicit biases of the training algorithm and explicit regularization such as weight decay, this paper studies how the two interact. Appealing to previous works, they adapt the mirror flow framework for objectives with explici... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the reviewer’s time and effort in providing valuable comments on our manuscript and appreciate the acknowledgement of the work's relevance and novelty. We would be happy to discuss any open questions or concerns, if there remain any.
**Background knowled... | Summary: The paper investigates how external regularization influences the implicit bias of gradient flow. By leveraging the equivalence between parameterized gradient flow and mirror flow, the study provides a detailed analysis of how external regularization alters this mirror flow. Within the framework proposed by Li... | Rebuttal 1:
Rebuttal: We would like to express our gratitude for the reviewer’s time and effort in providing valuable comments on our manuscript and appreciate the acknowledgement of the theoretical contributions. Below, we address the raised questions and concerns, but would be happy to extend the discussion on reques... | null | null | null | null | null | null | null | null |
Provably Mitigating Corruption, Overoptimization, and Verbosity Simultaneously in Offline and Online RLHF/DPO Alignment | Reject | Summary: Reinforcement learning from human feedback (RLHF) aims to align generative models with human preferences. However, the quality of alignment training can be compromised by corrupted preferences, reward overoptimization, and bias toward verbose outputs. This paper proposes RLHF-COV and DPO-COV, two algorithms de... | Rebuttal 1:
Rebuttal: **Claims And Evidence (1):** The study explores only a narrow set of tasks, primarily math and reasoning, with minimal analysis. Key questions remain: How does the algorithm perform on a broader range of tasks?
**A:** Thanks for your question. We are conducting experiments on the new tasks.
**C... | Summary: The paper studied corruption, overoptimization, and verbosity simultaneously in offline and offline LLM alignment problems (RLHF and DPO). The authors give both theoretical and empirical guarantees.
Claims And Evidence: The claims are mostly correct.
Methods And Evaluation Criteria: They make sense.
Theoret... | Rebuttal 1:
Rebuttal: **Questions For Authors (1):** Please discuss and compare your results with [1] for robustness.
[1] Chowdhury, Sayak Ray, Anush Kini, and Nagarajan Natarajan. "Provably robust DPO: Aligning language models with noisy feedback." ArXiv:2403.00409 (2024).
**A:** Thanks for bringing this important w... | Summary: The authors identify three key challenges in LLM alignment: corruption, overoptimization, and verbosity. To address these issues holistically, they propose a unified approach through generalized formulations of RLHF and DPO called RLHF-COV and DPO-COV, respectively.
These formulations incorporate: noise model... | Rebuttal 1:
Rebuttal: **Claims And Evidence:** The only claim I find a bit problematic is the verbosity reducing one. The main issue is that verbosity reduction is addressed through a universal length penalty which would not take into account prompt-specific length requirements. The results don't include numbers on ave... | Summary: This paper introduces RLHF-COV and DPO-COV to mitigate performance degradation caused by corrupted preferences, reward overoptimization, and bias toward verbosity.
To this end, the authors apply three techniques: a noise regularizer to enhance robustness against corrupted preferences, pessimistic MLE to handl... | Rebuttal 1:
Rebuttal: **Experimental Designs Or Analyses (1):** To show the LC-win rate, Argilla-DPO-Mix-7K is used, while performance is compared using reasoning tasks such as GSM8K and ARC. Why are different tasks required?
**A:** Argilla-DPO-Mix-7K is the preference dataset we use for training, while GSM8K and ARC ... | null | null | null | null | null | null |
IT$^3$: Idempotent Test-Time Training | Accept (poster) | Summary: This paper introduces IT3, a test-time training method that leverages idempotence to adapt model weights on-the-fly without requiring domain-specific auxiliary tasks. By enforcing that repeated applications of the model yield the same output, IT3 effectively projects out-of-distribution inputs onto the trainin... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments. We address the reviewer's concerns below and will revise the paper accordingly.
### **"The experiments primarily involve smaller datasets and omit standard OOD benchmarks like WILDS, leaving broader applicability and real-world relevance ... | Summary: This paper proposes a test-time adaptation (TTT) method named Idempotent Test-Time Training (IT3), that enforces idempotence to the model. Here, idempotence indicates that repeating a function to end in a stationary point (or fixed point). Specifically, IT3 predicts the output with a given input and randomly s... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed and constructive feedback. We address the reviewer's concerns below and will revise the paper accordingly.
### **"Lack of justification for IT3’s generalization ability to out-of-distribution (OOD) data. While the paper presents IT3 as a... | Summary: This paper proposes a novel test-time learning objective based on idempotent learning. The pipeline is easy to use, appears to be task-agnostic and model-agnostic, and is thus more versatile than previous TTA solutions. Experiments across various tasks demonstrate the effectiveness of the proposed method. My d... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and encouraging feedback. We address the reviewer's concerns below and will revise the paper accordingly.
### **"The authors claim that their method does not require designing additional auxiliary tasks or using extra data, making the ... | Summary: The authors:
1. Make the claim that enfocing idempotence is benificial for test-time training tasks.
2. Design a paradigm that brings an auxiliary signal representing ground truth, and force the network to learn idempotence through minimizing $\Vert f_{\theta}(x, y) - y \Vert + \Vert f_{\theta}(x, 0) - y \Ver... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. Below we address all the comments, and will revise the camera-ready version accordingly.
### **"The paper's presentation lacks clarity. Specifically, one of its key claim is "if such a network is trained so th... | null | null | null | null | null | null |
Signed Laplacians for Constrained Graph Clustering | Accept (spotlight poster) | Summary: This paper addresses the constrained graph clustering problem, where the goal is to partition a graph into clusters while incorporating domain knowledge in the form of MUST-LINK and CANNOT-LINK constraints. The authors establish a Cheeger-type inequality that relates the solution of the constrained clustering ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and and valuable suggestions. Here is our response to the raised questions:
**Response to _Methods and Evaluation Criteria_:**
We agree that further comparison between our work with the state-of-the-art constrained clustering algorithms wi... | Summary: The paper provides a spectral method for approximately optimising the cut ratio for a graph $G$ and a constraint graph $H$ using the smallest non-zero eigenvalues of two particular graph Laplacians. This is based on a proof of Cheeger-type inequality similar to the min-cut approach for a single graph. Practica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and valuable suggestions. Here is our response to the raised questions.
**Response to _Claims And Evidence_:**
> Why would sweep-cut versions based on these inequalities not work for the constraint clustering problem?
In our setting, sweep-c... | Summary: The paper considers the constrained graph clustering problem.
The input consists of two graphs, G and H, defined on the same set of nodes.
The clustering challenge is to group together nodes that are connected with large
weights in G and small weights in H.
The paper considers only clustering into two cluste... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and insightful questions. Here is our response to the raised questions:
> My main concern is with identifying applications of this approach. Where would the graph $H$ come from?
We agree that clarifying the role and origin of the constraint gra... | Summary: This paper considers the constrained clustering problems over two graphs.
This paper establishes the Cheeger inequality for the proposed algorithm for constrained clustering, which can be a counterpart of Cheeger inequality to the standard spectral clustering over a graph. The proposed algorithm improves spect... | Rebuttal 1:
Rebuttal: We thank the reviewer their positive evaluation, and constructive feedback. Here is our response to their questions:
**Response to _Relation To Broader Scientific Literature_:**
Our Cheeger-type inequality improves previous related results (including that of Koutis et al. (2023)). Our main the... | null | null | null | null | null | null |
Soup-of-Experts: Pretraining Specialist Models via Parameters Averaging | Accept (poster) | Summary: The paper proposes a method for combining a set of pretrained models by learning linear coefficients that are used to merge corresponding layer weights, thereby producing domain-specific architectures. In essence, the approach learns weighting coefficients similar to a router in mixture-of-experts models that ... | Rebuttal 1:
Rebuttal: Dear reviewer,
We thank you for your work reviewing our paper and for your comments. We are glad to read that "The experimental designs are well conceived", and we propose "a novel method that is both theoretically grounded and practically relevant".
We now try to address your concerns.
- "*the... | Summary: This paper introduces Soup-of-Experts, a method that trains a group of expert models to construct a specialized model for a given target domain. The specialized model is obtained by linearly combining the expert models in parameter space. The architecture consists of a set of expert models, a shared model, and... | Rebuttal 1:
Rebuttal: Dear reviewer,
We thank you for your review and for helping us improve the paper. We are happy to read that "The experiment design and analysis are appropriate.", that "this paper delivers good results", and that "the proposed method is easy to understand".
We now try to address your concerns.
... | Summary: This paper studies the problem of training specialized models efficiently for a new domain. This is done by design from the pretraining stage by training a model in a style akin to mixture-of-experts. The data is sampled across domains in a mini-batch and each expert corresponding to each domain is updated, to... | Rebuttal 1:
Rebuttal: Dear reviewer,
We thank you for your review and for your insightful comments, which will help us improve the paper. We are happy to hear that "The paper is overall well-written" and that "The task setup that is being studied is very relevant and important."
We now address your concerns.
- "*t... | Summary: The authors propose Soup-of-Experts, which can quickly create a specialized model for a given mixture of data domain weights at test time. Soup-of-Experts jointly pretrains and learns a function to compute weights for a linear combination of expert model weights for specialization.
## update after rebuttal
I ... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thanks a lot for your review and for your questions and remarks; they will help us improve the paper. We are please to see that you found that the main claim is well supported, that "the proposed method makes sense", and that the experiments are well designed.
We now answer your q... | Summary: The authors introduce "Soup-of-Experts" a method for pretraining language models so that they can quickly instantiate small, specialized models for different domain distributions. The architecture consists of a shared base model, multiple expert parameter sets, and a small learned routing function (an MLP) tha... | Rebuttal 1:
Rebuttal: Dear reviewer,
First, we thank you for your work, and for your remarks that will help us improve our work. We are happy to hear that *"the claims to be supported by sound evidence"*, that *"the methods and evaluation criteria are well aligned with the stated goal"*, and that *"Experiments are me... | null | null | null | null |
Closed-Loop Long-Horizon Robotic Planning via Equilibrium Sequence Modeling | Accept (poster) | Summary: This paper introduces a novel approach for long-horizon robotic task planning using LLMs with Equilibrium Sequence Modeling. The core idea is to view planning as a self-refinement process converging to a fixed-point (equilibrium) plan that integrates feedback from the environment or a world model. Rather than ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for providing valuable feedback. Below are our responses to the raised concerns.
[W1] Existence and uniqueness of equilibrium points
* We empirically confirm that equilibria always exist across different initializations and tasks (see Table 5 in the appendix).... | Summary: In this paper, a closed-loop long-horizon robot planning method based on equilibrium sequence modeling is proposed. By treating the planning as a self-optimizing fixed-point solution process, the implicit gradient of the deep equilibrium model is leveraged for end-to-end supervised training without the need fo... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive feedback. Below are our responses to the raised concerns.
[W1] Generalization to environment without feedback
* There are two workarounds in the absense of environmental feedback: (1) Use zero-shot LLM feedback during training. Since LLMs have been sh... | Summary: This paper introduces "Equilibrium Sequence Modeling," a novel framework for robotic task planning using Large Language Models (LLMs). The authors propose reformulating plan refinement as a fixed-point problem solvable with deep equilibrium models, enabling a supervised training scheme for improving self-refin... | Rebuttal 1:
Rebuttal: Thank you for the very constructive comments. Below, we first address the raised questions.
[Q1] Advantage of combined feedback
* The world model's feedback offers an alternative to environmental feedback when the interaction budget is limited. By combining both feedbacks, the planner can perform... | Summary: This paper proposes an equilibrium model-based planner for decomposing high-level tasks into mid-level action sequences in an iterative manner taking environment and world model feedback. Experiments on VirtualHome-Env benchmark demonstrates that the approach can improve over a few existing approaches.
Claim... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable suggestions, and we would like to address your main concerns as follows:
[W1] Connection to real-world robot control
* Our work focuses on high-level planning that decomposes each task into mid-level actions, as a complementary direction to low-level control. E... | null | null | null | null | null | null |
Core Context Aware Transformers for Long Context Language Modeling | Accept (poster) | Summary: This paper addresses the challenge of long-context language modeling by proposing a Core Context Aware Attention (CCA-Attention) mechanism. The method improves efficiency by dynamically selecting core tokens within token groups while preserving local information. CCA-Attention consists of two main components: ... | Rebuttal 1:
Rebuttal: > Q1. The paper only compares against training-free methods, important training-based approaches are missing. While the method achieves better latency and memory efficiency, the actual improvement in average score is marginal.
A1. We appreciate your emphasis on contextualizing our work against tr... | Summary: This work proposes Core-Context-Aware attention to enhance efficient long-context modeling for LMs. Two modules are included: a global pooling module to compresses groups of tokens into core tokens and a local module to preserve local information. Experiments on various tasks and datasets show the effectivenes... | Rebuttal 1:
Rebuttal: > Q1. The proposed methods share similarities to previous context compression methods[r1-r6]. r1: Compressive Transformers, r2: Gist, r3: NUGGET, r4: Landmark, r5: Beacon, r6: DODO.
A1. Thanks for your valuable comments. We clarify the differences below:
- **Problem Importance & Motivation**
The... | Summary: This paper addresses the computational inefficiency and redundancy in Transformer-based Large Language Models (LLMs) when processing extremely long contexts (e.g., 128K tokens). The authors propose Core Context Aware (CCA) Attention, a plug-and-play mechanism comprising two modules: (1) a globality-aware pooli... | Rebuttal 1:
Rebuttal: >Q1. Figure 3 demonstrates stable performance with varying group sizes (g) and window lengths (s) during inference, what are the possible reasons?
A1. We deeply appreciate your valuable feedback. We design our CCA-Attention for flexibility, enabling promising performance across varying $g$ and $s... | null | null | null | null | null | null | null | null |
LLMScan: Causal Scan for LLM Misbehavior Detection | Accept (poster) | Summary: The authors propose to detect misbehavior in LLMs through two major components. 1) They assess the contribution of individual input tokens and neural network layers on the final output. 2) They train a detector to classify prompts based on the properties of the analysis conducted in 1). They evaluate their app... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your insightful comments. Please find our responses to your questions below.
**Q1.** Computational overhead compared to the baseline approaches.
**A1.** We thank the reviewer for highlighting the importance of evaluating the computatio... | Summary: The authors present LLMScan, a technique to determine when the LLM is misbehaving via causal inference. They analyze the causal effects of input tokens by performing interventions on each token and measuring changes in attention scores, and in transformer layer by skipping the layer and comparing the output lo... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your insightful comments. Please find our responses to your questions below.
**Q1.** Causal map visualization showing both input tokens and layers.
**A1.** We thank the reviewer for their careful observation regarding the presentation ... | Summary: In this paper, the authors introduce a novel framework for detecting misbehavior in LLMs using causal analysis. In particular, the proposed framework consists of two main modules: i) a scanner that can conduct causality analysis at both token and model layer levels, and ii) a detector which is trained on causa... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your insightful comments. Please find our responses to your questions below.
**Q1.** Concerns on LLM is induced to misbehavior (e.g., lie) with instruction.
**A1.** We thank the reviewer for the careful evaluation and for raising an imp... | Summary: This paper proposes a detection mechanism named "LLMSCAN" for identifying potential "undesirable" generation behaviors during large language model (LLM) inference. The core approach is to utilize causal analysis to "intervene" in the input tokens and the transformer layers of the model, and to measure the caus... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and for your insightful comments. Please find our responses to your questions below.
**Q1.** API access scenarios.
**A1.** We thank the reviewer for raising this important and practical question regarding the applicability of our method. Our m... | null | null | null | null | null | null |
Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework | Accept (spotlight poster) | Summary: The paper introduces FEDGVI, a novel probabilistic framework for federated learning that is designed for both prior and likelihood misspecification. ## update after rebuttal I will retain my original score.
Claims And Evidence: Good.
Methods And Evaluation Criteria: Fair.
Theoretical Claims: Good.
Experime... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of the clarity of our paper and the theoretical results, as well as their important suggestion for an ablation study that we have now conducted and which will significantly improve the empirical study of this work. We will include these in the revised v... | Summary: The paper introduces *FEDGVI* (Federated Generalized Variational Inference), a probabilistic federated learning framework designed to be robust against both prior and likelihood misspecification. FEDGVI generalizes Partitioned Variational Inference by integrating robust and conjugate updates, thus reducing com... | Rebuttal 1:
Rebuttal: Thank you for appreciating our theoretical results and especially for taking the time to review our proofs.
**Claims & Evidence**
> the examples provided are [...] specifically chosen to highlight FEDGVI's strengths over PVI
FedGVI with the negative log likelihood and KL divergence is equivalen... | Summary: The paper presents a new framework, Federated Generalised Variational Inference (FEDGVI), for robust probabilistic federated learning. The authors argue that standard Bayesian and frequentist federated learning methods are vulnerable to model misspecification (e.g., contaminated data, incorrect priors, or mism... | Rebuttal 1:
Rebuttal: We thank the reviewer for their in-depth, constructive, and positive review.
**Question 1, Weakness 1, and Experiments**
> ...method’s sensitivity to the choice of robust hyperparameters [...] how stable...
> How sensitive is FEDGVI to [...] hyperparameters [...]? Would small changes degrade pe... | Summary: This work presents FedGVI, an extension of partitioned variational inference (PVI) to generalised variational inference (GVI). The core benefit of GVI is that it permits robustness to model misspecification. The authors demonstrate a number of advantageous properties of FedGVI, most notably theoretical results... | Rebuttal 1:
Rebuttal: Thank you for your constructive and positive review that highlights the clarity of our writing, the theoretical results on FedGVI, and its provable robustness. By addressing below the weaknesses you mentioned, we notably improve the clarity of the paper and its ease of understanding for the reader... | null | null | null | null | null | null |
Navigating Semantic Drift in Task-Agnostic Class-Incremental Learning | Accept (oral) | Summary: This paper proposes a method to improve model stability in class-incremental learning by alleviating the semantic drift phenomenon. The authors leverage the transferability of pretrained models and train parameter-efficient fine-tuning LoRA modules with a frozen ViT backbone. They define semantic drift in two ... | Rebuttal 1:
Rebuttal: We thank Reviewer o87J for the valuable comments. Reviewer o87J gives a **positive rating (3-Weak Accept)**, finds our method is "reasonably designed" with "a novel, creative covariance calibration approach" and mathematical formulations are "well-defined and correct", etc. We address the main con... | Summary: This paper identifies that the feature distribution gap between novel and existing tasks is primarily influenced by differences in mean and covariance moments. To address this, a novel semantic drift calibration method is proposed, integrating mean shift compensation and covariance alignment. Specifically, a M... | Rebuttal 1:
Rebuttal: We thank Reviewer JCMA for the valuable comments. Reviewer JCMA gives a **positive rating (4-Accept)**, acknowledges "a novel semantic drift calibration method" which is "well-designed and effectively assessed" and notes that its "key components improve performance remarkably" offering "improved e... | Summary: Balancing flexibility and stability remain a key challenge in class-incremental learning (CIL). To address this, this paper introduces mean shift compensation and covariance calibration to regulate feature moments, preserving both model stability and adaptability. Additionally, a feature self-distillation mech... | Rebuttal 1:
Rebuttal: We thank Reviewer XH2d for the valuable comments. Reviewer XH2d gives a **positive rating (4-Accept)**, finds that "the paper is well-written and well-organized" with "a clear, straightforward motivation" coupled with "intuitive illustrations" and "demonstrate superior performance" with "careful e... | Summary: This paper tackles the challenge of class-incremental learning in continual Learning, which enables models to sequentially learn multiple tasks without retraining or accessing data from previous tasks.
While recent advancements in deep learning, such as larger model capacities and large-scale pretraining, hav... | Rebuttal 1:
Rebuttal: We thank Reviewer 31F2 for the valuable comments. Reviewer 31F2 gives a **positive rating (3-Weak Accept)**, finds "the proposed method demonstrates superior performance", and our experimental designs are "rigorous, diverse and comprehensive", etc. We address the main concerns below:
> **Reviewer... | null | null | null | null | null | null |
Does Data Scaling Lead to Visual Compositional Generalization? | Accept (poster) | Summary: The article investigates how the compositional generalization capacity of vision models is related to the scale and quality of the training dataset. The authors main contribution is the proposal of a systematic way to manipulate training and testing dataset splits in order to evaluate how data diversity promot... | Rebuttal 1:
Rebuttal: We appreciate your thorough review. We will incorporate the feedback into the updated manuscript.
**They do not test a large variety of models when training from scratch (only ResNet-50 is reported), but they claim to have used other baselines in the text. It would be good to include some of thes... | Summary: This paper studies whether compositional generalization emerges in vision models trained from scratch and large-scale datasets. The authors design simple experiments where two factors, i.e., shape and color, control the dataset. The authors explore different levels of compositions in the training set and offer... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We will incorporate these suggestions in the final version of the manuscript.
**"large pretrained models" can be very misleading**
We agree about emphasizing the vision aspect. However, our models are large within the vision domain—for example, DINO ViT-L cont... | Summary: This paper examines whether data scaling improves compositional generalization in vision models, emphasizing the role of data diversity. Through controlled experiments with synthetic datasets, the authors show that models can achieve compositional generalization, but this ability depends on training data diver... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We will clarify the raised points in the revised manuscript.
**L223: What is the significance of reporting average accuracy per epoch? Does it effectively reflect the model's final compositional generalization capability?**
Thank you for pointing this out. We... | Summary: Compositional generalization is a fundamental aspect of human intelligence, yet its emergence in machine learning, particularly in vision models, remains unclear. This paper investigates whether increasing data scale contributes to compositional generalization in vision models by systematically isolating the e... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We will add clarifications to the updated manuscript.
**Clarify of definitions**
In our terminology, “concepts” refer to interpretable properties of the data (e.g., colour, shape, size), while “features” refer to the latent representations extracted by the mod... | null | null | null | null | null | null |
Local Pan-privacy for Federated Analytics | Accept (poster) | Summary: This paper explores three fundamental problems within the framework of local pan differential privacy: estimating the number of non-zero entries, calculating the mean, and constructing histograms. The authors begin by establishing a lower bound for counting non-zero entries, which includes a $\sqrt{T}$ factor,... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback, and for pointing out the typos in Thm 7, Thm 6.1, and in Algorithm 6. We will fix those in the updated version.
**Public-key vs. FHE**: the existing implementations of these primitives have very large gaps. Indeed the public-key encryption based ... | Summary: This paper introduces a privacy protection model termed local pan-privacy, aiming to address privacy leakage issues in federated analytics when local devices are subject to continuous intrusions. The authors first theoretically prove that achieving pan-privacy in an information-theoretic sense leads to estimat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback.
**Choice of tasks**: In this work, we have focused on the simplest and most natural tasks in this new model. These histogram-type tasks have also been used in federated analytics applications to enable a larger class of statistical tasks. To our ... | Summary: The paper introduces and examines the concept of *local pan-privacy*, which assumes that an adversary can eavesdrop on a local device’s internal memory. As a result, clients must ensure that their *internal states* remain secure. Under this constraint, the authors investigate (federated) analytics problems suc... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback.
**Threat model**: We will clarify, motivate and illustrate the threat model. Our main motivating example is a shared device: a computer in a public library, a shared device in a home or other setting. The attacker is another user of the device, who... | null | null | null | null | null | null | null | null |
OrcaLoca: An LLM Agent Framework for Software Issue Localization | Accept (poster) | Summary: This paper proposes a novel framework for automated software issue localization tasks. Issue localization is a critical component of autonomous software engineering, making the problem addressed in this paper well-motivated. The proposed technique combines several strategies to improve localization accuracy an... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and for highlighting several key strengths of our work. We listed our responses for weaknesses and questions below:
**Weakness 1:** While the overall approach is innovative, parts of the paper are difficult to parse... Ensuring the paper is sel... | Summary: This paper introduces OrcaLoca, an LLM agent framework that improves software issue localization by integrating three key components: priority-based scheduling for LLM-guided actions, action decomposition with relevance scoring, and distance-aware context pruning. Experimental results demonstrate that OrcaLoca... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful and constructive feedback, as well as for highlighting several key strengths of our work. For the responses for weakness, we listed our analysis part by part below:
**Weakness 1:** The complexity of the approach
**Response:**
Thank you for your sugge... | Summary: The authors presented a new agentic framework called OrcaLoca to identify relevant code and resolve issues in software engineering problems. They introduce 3 new approaches: giving each action a relevancy score, maintaining a priority queue of actions where they are dynamically reordered based on contextual re... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful and constructive feedback, as well as for highlighting several key strengths of our work. We also thank you for raising this important point regarding the discrepancy between Function/File Match performance and the Resolved rate in baselines.
Generally ... | Summary: OrcaLoca is a novel framework that leverages LLM agents to improve software issue localization by precisely identifying the problematic sections within large codebases. The paper introduces three key innovations: priority-based scheduling for dynamically managing LLM-guided actions, action decomposition with r... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback and for highlighting several key strengths of our work. For the weak points, we list our responses part by part, as follows:
**Weakness 1:** Heavy reliance on LLM outputs can lead to unpredictability and occasional hallucinations, affecting con... | null | null | null | null | null | null |
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces | Accept (poster) | Summary: This work provides a geometric characterization of transfer learning in the form of 3 types of inductive transfers presented in a Hilbert space. A type 1 transfer is when the predictor of the target task is the Convex Hull of the source predictors, type 2 is when the target is the linear span of the source pre... | Rebuttal 1:
Rebuttal: 1. I think the paper can benefit from additional experiments in other domains, e.g. text, structured prediction.
Function encoders are applicable in many domains. However, there are additional theoretical questions on how to define an appropriate inner product and geometric characterization of ta... | Summary: This paper studies inductive transfer learning where the source tasks and the target task share the same domain $(\mathcal{X}, \mathbb{P}(\mathcal{X}))$ and output space $\mathcal{Y}$, but have different predictors $f:\mathcal{X}\to\mathcal{Y}$ that lie in some Hilbert space. It characterizes the difficulty of... | Rebuttal 1:
Rebuttal: 1. Originality of the proposed approach. The least squares method for function encoder training seems to already exist in the literature. See "Essential References Not Discussed" for more details.
We thank the reviewer for pointing out the referenced work in [1], and we have added the reference t... | Summary: The authors define three types of inductive transfer in Hilbert spaces: interpolation within the convex hull, extrapolation to the linear span, and extrapolation outside the span. They propose to learn neural network basis functions, named as function encoders, that can represent any function in this space. Sp... | Rebuttal 1:
Rebuttal: 1. I would suggest some additional experiments on the computational cost of increasing k.
Using the worst-case strategy, the compute time for a forward pass is linear in k if you compute the basis functions sequentially on a single thread. Beyond the cost for the forward pass, the compute time fo... | null | null | null | null | null | null | null | null |
Avoiding Catastrophe in Online Learning by Asking for Help | Accept (poster) | Summary: The paper addresses a novel problem of avoiding catastrophe in online learning with the help of a mentor policy. Specifically, for each input and action, there is a probability of catastrophe. The objective is to minimize the probability of catastrophe over T rounds of play while keeping the number of queries ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. We respond to each concern and question:
**Concern 1**
> The regret compared to the mentor makes sense on one hand; however, the fact that it becomes somewhat vacuous unless the mentor avoids catastrophe with a probability close to 1 is a bit odd…I... | Summary: This paper formula the events of catastrophe as product of payoff functions where each payoff function can be seen as the probability of catastrophe occurs at each time step. Apart from the multiplicative objective in different to standard regret in online learning, the agent observes feedback only when asking... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. We believe the reviewer articulates only a single weakness:
> Weakness: for the problem that is solvable, it generally relying on the VC / Littlestone Dimension as a parameter to Hedge Algorithm, which can be impractical to compute
We agree that th... | Summary: The paper studies how to design learning algorithms that avoid catastrophe. In particular, the goal of the learner is to minimize the chance of a catastrophe. This is modeled as an online learning problem in which the learner aims at minimizing the product of the payoffs. The learner is equipped with a mentor ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. We respond to each of the reviewer’s concerns:
**Concern 1**
> However, how the problem is modeled as an online learning problem with mentor is a bit arbitrary. I'm not sure if the right way to sell the paper is yours, i.e., you model the problem o... | Summary: This work introduces an online learning framework that avoids catastrophic mistakes by allowing an agent to query a mentor when uncertain, rather than relying solely on trial-and-error. The approach maximizes the product of payoffs, where each payoff represents the chance of avoiding catastrophe, and leverages... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review.
> Page 4: "Also, that work" ---> Also, their work
We appreciate the correction and will make this change.
> On the bottom of page 3 the authors state "sublinear regret is possible iff the hypothesis ...". Is this supposed to by an if and only if o... | null | null | null | null | null | null |
An Asymptotically Optimal Approximation Algorithm for Multiobjective Submodular Maximization at Scale | Accept (poster) | Summary: The paper develops a discrete, scalable algorithm for multiobjective submodular maximization under a cardinality constraint that nearly achieves the optimal (1−1/e) approximation ratio. Instead of relying on continuous relaxations such as the multilinear extension, the proposed approach builds the solution ite... | Rebuttal 1:
Rebuttal: Thank you for your comments and pointing out that our algorithm outperforms existing methods in terms of running time.
> The query complexity of O(nBk) appears suboptimal since B approaches n, potentially resulting in quadratic complexity. This does not seem sufficiently efficient for practical a... | Summary: This paper addresses the problem of maximizing the minimum over several submodular functions, known as multiobjective submodular maximization. The authors present the first scalable algorithm that achieves the best-known approximation guarantee. Additionally, they introduce a novel application—fair centrality ... | Rebuttal 1:
Rebuttal: Thank you for evaluation and the positive comments on our writing and the scalability of our approach.
> The technical novelty of the work seems marginal. While the proposed solution outperforms existing studies in terms of lower running time and improved approximation ratio, the fundamental desi... | Summary: They study the multiobjective monotone submodular maximization under cardinality constraint, where the goal is to select a subset of elements of limited size that maximizes the minimum submodular value among the given functions. Previously, a $(1-\frac{1}{e})$-approximation algorithm was known for this problem... | Rebuttal 1:
Rebuttal: Thank you for your comments and the positive feedback on our presentation.
> However, it seems a greedy heuristics algorithm finds almost the same submodular value, while having better running time and query complexity.
> The only weakness is that the greedy minimum algorithm seems to outperfor... | Summary: A combinatorial algorithm for multi-objective submodular optimization is developed, that achieves ratio 1-1/e with constant problem under assumptions on the budget and number of colors. This improves over the best ratio achieved by a combinatorial algorithm in prior work. The algorithm requires several novel i... | Rebuttal 1:
Rebuttal: Thank you for your evaluation and the encouraging comments on the strengths of our paper.
> This work isn't essential, but I would appreciate a discussion of [1], as elaborated in strengths and weaknesses.
> [1]. Buchbinder, Niv, et al. "Submodular maximization with cardinality constraints." Proc... | null | null | null | null | null | null |
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton | Accept (oral) | Summary: This paper looks at how inversion bias (where the inverses of random sketches of a matrix are not unbiased) can be corrected for several random sampling methods. The paper gives an outline of how this can be done, and gives bounds for an $(\epsilon,\delta)$ estimator in Theorem 3.1 The paper makes a commentary... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her constructive and thoughtful comments. Please find our point-by-point responses below.
**Methods And Evaluation Criteria**:
The datasets are standard benchmark datasets (MNIST and CIFAR-10).
I do have a question about lines 400-402: "Figures 2 and 3 do not incl... | Summary: The paper studies the row-sampling of matrices in the context of approximating the inverse. Specifically, given an $n \times d$ matrix $A$ and a row-sampling matrix $S$ with $m$ rows, sketching is commonly used to approximate $A^T A$ by $A^T S^T S A$. Prior work by Derezinski et al. showed that the inverse of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her constructive and thoughtful comments. Please find our point-by-point responses below.
**Theoretical Claims:**
I did not have time to check all the proofs. The following are a few questions:
1. Page 4, left column, Line 199: what does it mean to say “subspace ... | Summary: The paper studies debiasing scheme for sub-sampled random matrix sketching. It improves prior work by providing a novel random sampling method which has better convergence result. Experiments are presented to corroborate theoretic results.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theore... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her constructive and thoughtful comments. Please find our point-by-point responses below.
**Essential References Not Discussed**:
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing (ICLR 2025, https://a... | Summary: This paper analyzes and corrects the inversion bias arising from common random sketching methods, including uniform/weighted sampling (based on leverage scores) and structured fast Johnson-Lindenstrauss transform like the sub-sampled randomized Hadamard transform (SRHT). Via a fine-grained characterization of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his/her constructive and thoughtful comments. Please find our point-by-point responses below.
**Other Comments Or Suggestions**:
1. Are the constants C in the theorems, propositions, and lemmas in Section 2 and 3 the same, or at least on the same order? It would be hel... | null | null | null | null | null | null |
Learning curves theory for hierarchically compositional data with power-law distributed features | Accept (poster) | Summary: This paper uses a theoretical framework to explain the emergence of neural scaling laws. It builds on the synthetic model and results derived in previous works on classification (Cagnetta et al., 2024) and next-token prediction (NTP) (Cagnetta & Wyart, 2024) that studied how hierarchical tasks are learned. The... | Rebuttal 1:
Rebuttal: ## Weaknesses
1. As the reviewer points out, we work in the data-limited regime, which is suitable for studying learning curves. In all our experiments, the models are large enough and trained for long enough for the training loss to reach its lower bound. In particular, for consistency, we incre... | Summary: - Main algorithmic/conceptual ideas:
- Mostly based on the framework of Cagnetta et al., 2024.
- Main results/findings:
- This paper shows that Zipf distribution of feature exists (which is common in real world scenario for long-tailed distributions) the learning curve of classification will show a power... | Rebuttal 1:
Rebuttal: ## Theoretical claims
1. **compatible**. In line 86, compatible means that the sentences in the training data can be generated both by a PCFG and by a non-hierarchical generative model. The next 4 occurrences of "compatible" in the main refer to compatibility of a single token ($x_d$) with the co... | Summary: This paper presents a theoretical model to explain the emergence of power-law learning curves in deep neural networks trained on data with Zipfian feature distributions and hierarchical compositional structure. By parameterizing the Random Hierarchy Model (RHM) with a probabilistic context-free grammar (PCFG),... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our study's strengths. Regarding the connection with real data, please see the reply to Reviewer GeDB.
Concerning experiments on natural datasets, our work predicts that removing rare words from a data set should not affect the scaling law of the learning cu... | Summary: This paper extends the findings in (Cagnetta et al., 2024) to a probabilistic context-free grammars (PCFGs) case. The authors investigate how the structure of data influences learning curves, focusing on datasets with hierarchical compositional structures and features distributed according to power laws (Zipf ... | Rebuttal 1:
Rebuttal: **1st question:** For deep CNNs, $H=512$ (1M parameters in total, see last sentence of the second paragraph in appendix A), and we checked that our conclusions do not change up to $H=1024$ (~4M parameters). For transformers, $n_h=16$ (resulting in ~3M parameters), and we checked that our conclusio... | null | null | null | null | null | null |
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks | Accept (spotlight poster) | Summary: Recent research has shown that large language model (LLM)-based multi-agent systems outperform traditional single-agent methods. This paper focuses on the challenge of choosing effective communication topologies for multi-agent systems. It puts forward the LLM-based Multi-agent Communication Protocol (MACP), w... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful comments and thorough understanding of our paper! Here we give point-by-point responses to your comments and describe the revisions we made to address them.
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> **`Weakness 1`**: Would switching between different agent roles have an impact on the experime... | Summary: This paper introduces G-Designer, an framework for designing task-aware, adaptive, and robust communication topologies for multi-agent systems powered by large language models (LLMs). G-Designer dynamically generates communication structures for specific user requests using a variational graph auto-encoder (VG... | Rebuttal 1:
Rebuttal: We would like to express our deepest respect for your meticulous review! In response to your efforts, we have carefully prepared a point-by-point reply:
---
> **`Weakness 1`**: Supplement more agent-specific benchmarks.
Thank you very much for your insights. We conducted experiments on the more ... | Summary: This paper introduces G-Designer, an innovative solution for multi-agent communication topology design in LLM-MAS. The authors first propose the Multi-agent Communication Protocol (MACP), which sets standards for LLM-MAS topology design in terms of effectiveness, complexity-adaptiveness, and adversarial robust... | Rebuttal 1:
Rebuttal: We sincerely thank you for the thoughtful and constructive reviews of our manuscript! Based on your questions and recommendations, we give point-by-point responses to your comments and describe the revisions we made to address them.
---
> **`Weakness 1`**: Temperature Settings in the Experiments
... | Summary: The paper discusses the advancements in collective intelligence among large language model-based agents, highlighting the challenge of selecting effective communication topologies for specific tasks. To address this, the authors introduce G-Designer, an adaptive solution that dynamically creates task-aware com... | Rebuttal 1:
Rebuttal: > **`Weakness 1`**: What happens to the communication protocol if a graph has a directed cycle?
Thank you for the insightful inquiry! In Equation (15), we extract an enforced acyclic graph $\mathcal{G}\_{com}$ from the potentially cyclic topology $\tilde{\mathbf{S}}$. Specifically, in our provide... | null | null | null | null | null | null |
Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems | Accept (poster) | Summary: Projection is a key methodology to understand polyhedral structure and reduce dimensionality in linear programming, with recent works proposing it as a heuristic to address large-scale models. The novelty of this work is in applying a machine learning technique to learn effective projection operators. The auth... | Rebuttal 1:
Rebuttal: We appreciate your positive and constructive comments.
> My major concern is that the authors have not interpreted much of the intricate generalization bound from Section 5.2, which can be difficult to understand. For example, the text could provide a table with a (rough) estimate of the bound fo... | Summary: This paper applies machine learning to find a more suitable projection of optimization models in the form of linear programming. By projecting to a space of smaller dimension, it is possible to solve a simpler version of the LP model much faster. A good projection should have basic solutions mapping to optimal... | Rebuttal 1:
Rebuttal: We appreciate your positive and constructive comments.
> I believe that evaluating the proposed NN architecture with random initialization and no training is an important benchmark. This would be different from the random projection used.
We compared with the proposed NN with random initializati... | Summary: This paper presents a data-driven method for reducing the dimensionality of linear programming (LP) problems by generating instance-specific projection matrices using a neural network-based model. The proposed approach aims to improve the efficiency of LP solvers by projecting high-dimensional LP instances int... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments.
> it lacks a clear comparison with existing MILP generation methods to highlight its distinct advantages.
Since MILP generation methods and our approach serve significantly different purposes, we cannot make direct comparison. We plan to add a discussion... | Summary: This paper proposed a new data-driven method to train a machine learning model to reduce the dimensionalities of large-scale linear programming problems (LP). This model not only can provide reduced dimension LP, by solving which one could obtain a high-quality feasible solution of the original LP, but can red... | Rebuttal 1:
Rebuttal: We appreciate your constructive comments.
> the authors only show the generalization bound but do not show the optimality of this model.
Since the objective function for training our model is non-convex, it is challenging to show the optimality of the trained model. Note that we can improve the ... | null | null | null | null | null | null |
AlphaPO: Reward Shape Matters for LLM Alignment | Accept (poster) | Summary: Authors step from likelihood displacement, a phenomenon in which both preferred and dispreferred responses drop likelihood during optimization. Derived from f-DPO, authors add length normalization to the alpha-divergence and show that alpha controls the likelihood displacement strengths through gradient analys... | Rebuttal 1:
Rebuttal: > The theorem does not provide proof ...
We acknowledge that the theorem can be made more clear. In particular, monotonicity can be proved for $T_1(\alpha)$; see the summary table [here](https://i.imgur.com/aW2jjkf.png). In general, the gradient magnitude is not monotonic as presented in Illustra... | Summary: The paper introduces AlphaPO, a novel preference training algorithm designed to improve the alignment of large language models (LLMs) by modifying the shape of the reward function used in preference optimization.
Unlike existing methods like Direct Preference Optimization (DPO) and Simple Preference Optimiza... | Rebuttal 1:
Rebuttal: > The main weakness is that the absolute improvement on SimPO is limited. If the paper can address the stability issues of SimPO like method (choice of hyper-parameter), it would be making a more impactful contribution.
We thank the reviewer for the comment. We would like to point out that our im... | Summary: The paper introduces AlphaPO, a variant of f-DPO that adopts $\alpha$-divergence and length normalization. The paper shows that varying $\alpha$ affects the shape of the implicit reward. With an appropriate value of $\alpha$, AlphaPO can mitigate the over-optimization issue of Direct Alignment Methods.
Claims... | Rebuttal 1:
Rebuttal: > In section 3.3 the authors mention "large values impose a regularization effect ...
We thank the reviewer for their insightful comment. The reviewer is right to point out that IPO and SLiC are important methods. The reasons we did not include them in the paper are (1) the SimPO paper already co... | null | null | null | null | null | null | null | null |
Efficient Multivariate Robust Mean Estimation Under Mean-Shift Contamination | Accept (poster) | Summary: In this paper the authors look at the problem of robust mean estimation under the mean shift contamination. Here one receives samples from a standard d-variate Gaussian with mean m such that m is $\mu$ with 2/3 probability and can be something else otherwise. The goal is to recover $\mu$ up to an $\epsilon$ er... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and time in assessing our work. We respond to the points raised individually below:
(**Experiments**) We would like to emphasize that our primary contribution is to characterize the computational-statistical landscape for this fundamental learning task—in te... | Summary: The authors consider the problem of robust mean estimation of an identity covariance Gaussian in the presence of mean-shift contamination. They specifically give the first computationally efficient algorithm for high-dimensional robust mean estimation with mean-shift contamination. Their algorithm has near-opt... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and for their positive assessment of our work. Below, we provide a clarification in response to their comment.
The introductory proof sketch mentions that we can assume $\lVert\mu\rVert = O(1)$ due to naive outlier removal. This means that even if $\lVert\mu\r... | Summary: This paper continues the recent line of work in robust mean estimation in high dimensions, with focus on getting arbitrary small (eps) error in the mean despite an alpha-fraction (for alpha in [0, 0.49]) of outliers. The more commonly studied Huber model for outliers contamination can only achieve O(alpha) er... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort and their positive assessment of our work. We respond to the comments below:
* (**Comparison with Lai, Rao, and Vempala 2016**) As we discuss in detail in Appendix (lines 662–671), although Lai, Rao, and Vempala (2016) iteratively reduce the dimension using ... | Summary: This work studies a relaxed model of robust mean estimation of a spherical Gaussian with identity covariance, where the adversarial points do not follow arbitrary distribution Q, but are sampled from $\mathcal{N}(z_i, I)$, where $z_i$ is an arbitrary point. Authors show that in such model consistent estimation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, effort and their clarifying questions. We comment on the points mentioned and respond to the reviewers questions below:
(**Identity covariance**) As pointed out by the reviewer, our algorithm works under the assumption that the covariance of the inliers is kn... | null | null | null | null | null | null |
Hyperband-based Bayesian Optimization for Black-box Prompt Selection | Accept (poster) | Summary: This paper assumes a candidate instruction set and a candidate example set and aims to optimize prompts by automatically selecting the best instruction + example combination. The method combines Bayesian Optimization with Hyperband, where the main contribution compared to previous work is using Hyperband to di... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review.
We are pleased that the reviewer finds our method efficient, acknowledges the strength of our experimental results, and appreciates the clarity of the paper.
Below, we respond to the concerns raised in the review.
`1. The setup assumes that we ... | Summary: This paper contributes in two main ways for prompt optimization in the context of maximizing total scores over datasets (e.g. GSM8K):
* Introducing a deep kernel GP, i.e. project high-dimensional prompt embeddings into a lower dimension before sending vectors to kernel
* Introducing standard Hyperband tech... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review.
We are pleased that the reviewer recognizes our structure-aware deep kernel GP as a novel and meaningful contribution and that the integration with Hyperband is viewed as sound and well-justified.
We also appreciate their acknowledgment of the c... | Summary: The paper introduces HbBoPs, a novel method for optimizing prompt selection in large language models (LLMs) in black-box settings. The method combines a structural-aware deep kernel Gaussian Process with Hyperband, a multi-fidelity scheduler, to efficiently select prompts. The approach is designed to handle la... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and for recognizing the novelty and efficiency of our proposed method.
While we appreciate the reviewer’s feedback, we note that some contributions of the paper may not have been fully recognized.
Below, we respond to the two concerns raised in... | Summary: The paper uses hyperband, combined with a Gaussian process surrogate model with deep kernel learning, to optimize the prompt for black-box LLMs. The method optimizes both the instruction and exemplars, and specifically aims to optimize the efficiency in terms of the total number of LLM calls. Extensive experim... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review.
We appreciate the reviewer’s recognition of the extensiveness of our experiments, as well as their assessment that our claims are supported by clear and convincing empirical evidence.
Moreover, we are happy to hear that they find the structural-... | null | null | null | null | null | null |
Joint Localization and Activation Editing for Low-Resource Fine-Tuning | Accept (poster) | Summary: The paper proposes JOLA, a novel parameter-efficient fine-tuning (PEFT) method that dynamically selects and edits the outputs of specific Transformer attention heads. JOLA jointly learns: (1) which heads to target, (2) the intervention type (additive, multiplicative, or both), and (3) the corresponding paramet... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and suggestions
**Q1: Presentation in Figure 1, Table 2.**
**A1:** Thank you for your suggestion. We will make the necessary changes in the next version.
**Q2: Relationship between PEFT?**
**A2:** In the abstract, we introduced PEFT and its limitations in... | Summary: This paper introduces JOLA, a novel approach to efficiently adapt large language models in low-resource settings. The method jointly learns which attention heads to modify and determines optimal activation interventions using both additive and multiplicative adjustments. By incorporating a dynamic gating mecha... | Rebuttal 1:
Rebuttal: We appreciate your comments and the opportunity to clarify and improve our work.
Q1: Huge difference in hyperparameter selection among the baseline methods and JoLA. The reproducibility of our proposed JoLA
A1: As noted in the original papers for each baseline method, these methods are sensitive... | Summary: The paper proposes JORA, an interpretability-inspied parameter-effecient tuning methods. JOLA intervenes on the attention activations with both scaling and offsetting. In addition, JOLA uses HardConcrete gates with expected-L0 regularization to learns the localization together with intervention in an end to en... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and provide valuable insights.
Q1: Baseline setup seem either under-tuned or too weak to compare with?
A1: Thank you for your feedback on our baseline system setup.
(1) As noted in the original papers for each baseline method, these methods are ... | Summary: This paper presents a novel extension of the activation editing approach. Its primary contribution lies in integrating localization and editing into a single process using a gating mechanism, unlike previous two-stage methods that first manually locate and then edit model components. This makes the proposed me... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestions.
**Comment:** The parameterized form of the hard-concrete distribution is not explicitly detailed. The trainable parameters of the gating mechanism are not clearly stated.
**Response:** The hard concrete distribution has two associated scalar paramet... | null | null | null | null | null | null |
When and How Does CLIP Enable Domain and Compositional Generalization? | Accept (spotlight poster) | Summary: This paper investigates the domain generalization and compositional generalization capabilities of CLIP models, focusing on how the diversity of training domains affects their ability to generalize to unseen domains and unseen class-domain combinations. The authors systematically construct training distributio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback. Below we address remaining concerns.
> W1: Comparisons with other vision-language models (VLMs)
We focused on CLIP due to its wide adoption and use in prior related work (e.g., [1, 2]).
We had already verified the consistency of ... | Summary: This paper tries to find what factors affect the domain generalization and compositional generalization of CLIP. The empirical experiment show that domain diversity is essential for both domain and compositional generalization.
Claims And Evidence: I'm not convinced that the experiments alone back the claims.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Below, we try to address the concerns. However, the review has been very brief, and some points remained unclear to us. We would greatly appreciate further clarification during the discussion phase so we can fully address them, if we have not already done so... | Summary: The paper studies the generalization capabilities of CLIP in the Domain Generalization setting. Specifically, the authors study when and how clip exhibits domain generalization - when a model generalizes to unseen domains, and compositional generalization - when a model generalizes to classes from partially se... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and constructive feedback. Below, we address the remaining concerns and questions.
> Can the CLIP visual features directly be considered for the top-k computation and further analyzes?
Yes, this is possible. However, we would like to note that CLIP’s visu... | null | null | null | null | null | null | null | null |
Self-Disentanglement and Re-Composition for Cross-Domain Few-Shot Segmentation | Accept (poster) | Summary: The paper finds that previous approaches have an entanglement problem, which tends to bind source domain patterns together, making each one difficult to transfer. They analyzed and explained the feature entanglement problem from a new perspective of the natural decomposition of ViT. On this basis, self-disenta... | Rebuttal 1:
Rebuttal: ## 1. Design for the cross-domain part
Our method design also highlights the cross-domain part of CDFSS in the following aspects.
(1) Intuitively, for the cross-domain transferability, the overall semantics, like a whole bat in Fig.1, is much harder to transfer than the separated parts of it, l... | Summary: This paper addresses the challenge of feature entanglement in Cross-Domain Few-Shot Segmentation by leveraging the inherent structure of Vision Transformers. The authors identify that current methods often suffer from entangled semantic patterns, which hinder the transferability of features across domains. To ... | Rebuttal 1:
Rebuttal: ## 1. Orthogonal loss weight
We validated the impact of the weight of the orthogonal loss on performance. The results indicate that the optimal choice of the weight is in a wide interval, which means the tuning of this hyper-parameter is not difficult. Additionally, we used the same orthogonal lo... | Summary: This paper addresses feature entanglement in Cross-Domain Few-Shot Segmentation (CD-FSS), discovering that ViT features assign equal weights to both meaningful and meaningless pattern matches when comparing images. To solve this, the authors propose a self-disentanglement and re-composition framework with thre... | Rebuttal 1:
Rebuttal: ## 1. Compare with more disentangle-based methods and Discussion on slot attention
Our approach differs from slot attention-based methods [1, 2] in two fundamental aspects: 1) Slot attention primarily disentangles distinct objects through object-centric representation optimization, exhibiting coa... | Summary: The paper addresses the feature entanglement problem in Cross-Domain Few-Shot Segmentation (CD-FSS) by leveraging the structural decomposition of Vision Transformers (ViTs). The authors identify that cross-layer comparisons in ViTs entangle meaningful and irrelevant patterns, leading to reduced transferability... | Rebuttal 1:
Rebuttal: ## 1. The theoretical analysis of the effectiveness of ViT disentanglement
In cross-domain few-shot segmentation tasks, models need to transfer knowledge from the **source domain $\mathcal{S}$** with abundant annotations to the **target domain $\mathcal{T}$** with limited data. Let $\mathcal{H}$ ... | null | null | null | null | null | null |
Causal Attribution Analysis for Continuous Outcomes | Accept (spotlight poster) | Summary: Previous studies have focused on attribution problems for binary outcomes, but binarizing continuous outcomes can lead to information loss or bias. To address this, the study introduces posterior causal estimands for evaluating multiple correlated causes in continuous outcomes. These estimands include posterio... | Rebuttal 1:
Rebuttal: ---
We sincerely thank the reviewer for the thoughtful feedback and strong accept recommendation. Your recognition of our contribution is very encouraging, and your suggestions are highly valuable for improving the paper.
---
**Q1: Path-Specific Effects and Pearl (2001)**
**A1:** Thank you ... | Summary: This paper focuses on the causal attribution analysis, which answers retrospective questions like "given that an outcome has occurred, how can we figure out how much each potential cause contributed to it?" Most existing literature on this are introduced with binary outcome variables, which is not the case in ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback and the positive recommendation. We appreciate your recognition of our contribution and your valuable suggestions, which we will address in the revised manuscript.
---
**Q1.** *The assumptions are strong, especially for Assumption 3.... | Summary: This paper addresses the causal attribution problem for continuous outcome variables, an interesting and realistic scenario compared to binary outcomes. It introduces a set of posterior causal estimands to retrospectively analyze causal attribution: PostTCE, PostNDE, PostNIE, PostICE. Under assumptions of seq... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive evaluation and constructive suggestions, which have been very helpful in improving the clarity and quality of our manuscript.
---
**Q1.** Discuss relation to essential references.
**A1.** Thank you for pointing out the related work by Schamberg... | Summary: The submission describe a method for counterfactual analysis for continuous outcomes.
Identifiability conditions and results are given. Moreover based on minimization of a certain loss the authors propose an estimator and derive some theoretical properties.
A practical example using simulated data show the res... | Rebuttal 1:
Rebuttal: ---
**Q1.** *The paper is well written and clear; it can be followed well. The originality is somehow restricted to the extension of a previous approach to continuous outcomes.*
**A1.** We sincerely thank the reviewer for the positive and encouraging feedback. We truly appreciate your kind recog... | null | null | null | null | null | null |
Discovering Global False Negatives On the Fly for Self-supervised Contrastive Learning | Accept (poster) | Summary: This paper presents an approach to detect false negative pairs in contrastive learning. Contrary to previous approaches, they work by detecting FN globally on the pretraining dataset, and they are computationally efficient as they apply SGD for each anchor and do not require to compute clustering for the whole... | Rebuttal 1:
Rebuttal: Thanks for your review and questions.
**Q1: It would have been nice to see other downstream tasks than classification to validate further the quality of the learned representation.**
**A:** Thanks for pointing this out. We agree that the unimodal performance has only been validated in the contex... | Summary: Previous contrastive learning methods may generate negative sample pairs with similar semantics when constructing negative samples. Different from them, this paper introduces a method that automatically learns on the fly the threshold for each anchor data to identify its false negatives during training. Meanwh... | Rebuttal 1:
Rebuttal: Thanks for your review and questions.
**Q1: About GloFND limitations to a specific contrastive learning (CL) technique and integration on classical contrastive learning algorithms.**
**A:** Thanks for this question, this is something we could have done a better job explaining. To clarify, the c... | Summary: In this work, authors propose GLOFND, a way to find and automatically threshold false negative samples during self-supervised training with contrastive learning. The proposed method works by determining adaptive thresholds $\lambda_i$ for each anchor $i$ which, thanks to the optimization-based approach, are gl... | Rebuttal 1:
Rebuttal: Thanks for your review and questions.
**Q1: Experiment on larger dataset than ImageNet**
**A:** We would like to point the reviewer to Table 3, where we tested GloFND on CC3M, which is larger than ImageNet-1k, with 2.7 million image-text pairs.
**Q2: GLOFND applied on other baselines (SimCLR, ... | null | null | null | null | null | null | null | null |
Incremental Gradient Descent with Small Epoch Counts is Surprisingly Slow on Ill-Conditioned Problems | Accept (poster) | Summary: This work investigates the convergence of shuffling gradient methods, especially focusing on the small epoch regime. The authors establish several new upper/lower bounds that are matched to each other, providing new insights into the finite-sum optimization problem.
## update after rebuttal
I keep my positiv... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the positive feedback. Below, we address the reviewer’s comments.
1. **The exponential term in Theorem 4.6 of [Liu et al., 2024] is in the order of $LD^2 \exp(-K/\kappa)K^{-1}$, which improves a factor of $K$.**
- Thank you for pointing out this issue. We have r... | Summary: This paper studies the Incremental Gradient Descent, a permutation-based SGD method. The authors derive a lower bound on the algorithm's progress when the number of epochs is small. This paper provides results for various classes of problems, when 1. all he component functions are strongly convex, 2. all compo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for the constructive feedback. Below, we address the reviewer’s concerns.
1. **Similar lower bounds can be derived for other algorithms like SGD (with uniform sampling) in the small epoch regime.**
- As the reviewer pointed out, it is true that any algorithm (includ... | Summary: The paper is inspired by the common use of shuffling-based methods such as Random Reshuffling in practice, and the authors provide new theoretical results for specific permutations. In particular, they give new lower bounds on the Incremental Gradient (IG) method in the low-epoch regime, which wasn't considere... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful review and for the positive assessment of our paper. Below, we address the reviewer’s concerns and comments.
1. **The main limitation of the work, in my opinion, is that it considers the least interesting of the shuffling methods.**
- We agree with the re... | Summary: This paper analyzes Incremental Gradient Descent (IGD) method in various convex settings, and establishes lower bounds of IGD in the small epoch regime and large epoch regime. This paper also provides upper bound results for arbitrary permutation-based SGD in several small epoch and large epoch regimes. This p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful review. Below, we address the reviewer’s concerns.
1. **The storyline of paper is not so clear.**
- While our analysis does involve three different permutation schemes—Incremental Gradient Descent (IGD), arbitrary permutation-based SGD, and Herding at ... | null | null | null | null | null | null |
Self-Bootstrapping for Versatile Test-Time Adaptation | Accept (poster) | Summary: This paper proposes Self-bootstrapping for versatile test-time adaptation, a general TTA framework that adapts models across classification, regression, and dense prediction tasks without requiring source data. SPA introduces weak-to-strong self-bootstrapping learning, ensuring adaptation by aligning a deterio... | Rebuttal 1:
Rebuttal: >Q1. Results on CIFAR-100 and ResNet
In the submission, we compare SPA on ImageNet-C/R/Sketch/A as they are large-scale, more challenging than CIFAR-100, and commonly used. We now report more results on CIFAR100-C below to further validate SPA. **Pls see Reviewer 61cM's Q1 for more ResNet results... | Summary: The paper explores the impact of typical distribution shifts on the information content of images across different spatial frequencies in the Fourier domain. It highlights that low-frequency components dominate in terms of information power, and removing these components provides more effective learning signal... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable feedback and your recognition of the novelty and contributions of our work for designing a challenging versatile fully TTA framework. Our SPA incorporates several components, including an active, deterioration-driven self-bootstrapping scheme (distinct from featu... | Summary: The paper introduces Self-Bootstrapping for versatile Test-Time Adaptation (SPA), a novel framework that enables TTA across multiple tasks—classification, segmentation, and 3D detection. The authors propose a geometry-preserving augmentation strategy using low-frequency amplitude masking and high-frequency noi... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable feedback and constructive comments on improving the quality of our paper. We would like to address your questions below.
>Q1. Comparison with TTT-series methods.
Thank you for your suggestion. We follow the comparison setting used in Diffusion-TTA and directly... | Summary: This paper proposes an image augmentation strategy utilizing the Fourier domain for randomly masking the low-frequency amplitude of an image. Further, it augments the image with noise injection to account for the lack of learning signals at high frequencies.
The paper reports experimental results on classifica... | Rebuttal 1:
Rebuttal: We deeply appreciate your valuable feedback and constructive comments on improving the quality of our paper. We would like to address your questions below.
>Q1. More classification results on ResNet-50.
Thanks for your suggestion. We conduct additional experiments on ResNet-50 and compare with m... | null | null | null | null | null | null |
TRACE Back from the Future: A Probabilistic Reasoning Approach to Controllable Language Generation | Accept (poster) | Summary: This paper introduces TRACE, a framework for controllable text generation that combines a distilled Hidden Markov Model (HMM) with lightweight classifiers to guide language model outputs toward desired attributes. The core idea is to compute the Expected Attribute Probability (EAP) tractably via HMM forward-ba... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback.
```
Detoxification: Experiments on GPT-2 and Gemma-2B are thorough, but toxicity evaluation lacks diversity (e.g., no human judgments).
```
We ran LLM-as-judge evaluations with GPT-4 to evaluate the toxicity, fluency, and diversity of the generated continuati... | Summary: The paper proposes a new method for controlled language modeling. Motivated by the compute and data inefficiency of previous solutions, the paper introduces a new method called TRACE, which uses conditional probabilities from an HMM to adjust token probabilities such that the text demonstrates desired attribut... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback.
```
Is log-MSE a standard method in this case? Can you cite a reference?
```
The concept of log-MSE is somewhat analogous to mean squared logarithmic error in the context of regression. It shares the intuition of penalizing more heavily one type of mispredictio... | Summary: This paper proposes TRACE, a new algorithm for controllable generation that uses a hidden markov model and a small classifier to "look ahead" and reweight the language model's token probabilities. They compare to many other controllable generation methods and report promising results on two types of controllab... | Rebuttal 1:
Rebuttal: Thank you for the constructive feedback.
```
Why evaluate on these specific domains (toxicity + political text)?
```
In the related literature, papers have evaluated on many different tasks rather than (a set of) fixed common benchmarks. We selected toxicity evaluation as it is a widely recognize... | Summary: Large language models (LLMs) are increasingly being deployed in real-world applications, and the need to control their outputs to align with human values is becoming more important. However, current autoregressive models struggle when attempting to control their generations. This paper proposes a technique cal... | Rebuttal 1:
Rebuttal: Thank you for the detailed review.
```
Table 1 contains several numbers struck out with asterisks, and it's unclear what that signifies.
```
We explained this in Lines 261-274 (left) in the main text but forgot to add it to the Table caption - we will do this in the revision.
```
a) Comparison to... | null | null | null | null | null | null |
Deep Bayesian Filter for Bayes-Faithful Data Assimilation | Accept (poster) | Summary: In order to address the challenge of nonlinear filtering, the present work proposes _Deep Bayesian Filter (DBF)_, which leverages a learnable inverse observation operator (IOO) to transform the problem into a linear filtering problem, where one can apply standard Kalman methodologies. The cases of linear and n... | Rebuttal 1:
Rebuttal: We deeply appreciate Reviewer gEdD for their thoughtful and constructive feedback. Below, we specifically address the reviewer’s important concerns.
- Scalability in High-Dimensional Settings:
We have conducted experiments varying latent dimensions in both the double pendulum and Lorenz96 settin... | Summary: This paper proposes a method for Bayesian filtering with nonlinear observations and dynamics, using a VAE specialized to Markov processes with linear latent dynamics. The result is a closed-form loss that can be optimized for both the encoder (latent state) and decoder (inverse observation operator, IOO).
Cla... | Rebuttal 1:
Rebuttal: We gratefully acknowledge Reviewer VtfJ for the positive evaluation and insightful feedback.
- Direct prediction of $o_t$:
We agree and will explicitly clarify that Eq. (6) is particularly useful for cases where direct observation prediction is sufficient, even when the mapping from $z$ to $o$ ... | Summary: The authors propose a novel variational method for data assimilation that constructs its variational family by replacing the non-linear observation model by a linear-Gaussian observation model whose mean and covariance are parametrized by a neural network.
If the dynamics of the prior are also linear-Gaussian,... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer zP6r for the thoughtful comments and constructive suggestions.
- Clarification on Section 2.3
We would like to respectfully clarify a key point regarding Section 2.3, which may have caused confusion due to insufficient clarity in our original exposition.
The reviewe... | null | null | null | null | null | null | null | null |
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization | Accept (oral) | Summary: The paper derives a novel variational inference scheme for optimizing the Max-value Entropy Search acquisition function in Bayesian optimization. Interestingly, for a particular choice of variational distribution, the classic Expected Improvement acquisition function shows up as a special case, thus showing it... | Rebuttal 1:
Rebuttal: Thank you for your support.
> There is a paragraph in 4.2 describing discrepancies between VES-Exp and EI. Is the EI being used here the LogEI? Earlier in the paper it said LogEI was being used, and used interchangeably with EI. If so that would be another discrepancy potentially worth noting. W... | Summary: The paper looks at Bayesian Optimization (BO) and tries to connect two types of acquisition functions that people have always thought of as different approaches. On one side, we have Expected Improvement (EI), which mostly focuses on exploitation by picking points that are likely to be better than what we've a... | Rebuttal 1:
Rebuttal: We sincerely thank you for the detailed comments and support on this work. Due to word limit constraints we will give brief answers:
> High computational cost
We are investigating the VarPro method, as detailed in our response to reviewer VbQ5.
> EGO and Snoek et al.
We will include them in t... | Summary: This paper introduces "Variational Entropy Search" (VES), a unified theoretical framework that reveals a previously unrecognized connection between Expected Improvement (EI) and information-theoretic acquisition functions in Bayesian optimization. The authors demonstrate that EI, traditionally considered disti... | Rebuttal 1:
Rebuttal: We sincerely appreciate your support for this work.
> the substantial computational cost disparity between VES (53.17s per iteration) versus EI/MES (~1-1.6s) limits practical applicability; the timeout issues on very high-dimensional problems (e.g., 1000D Lasso-Hard);
To address computational ef... | null | null | null | null | null | null | null | null |
KV Shifting Attention Enhances Language Modeling | Accept (poster) | Summary: - This paper proposes a new attention mechanism named KV shifting attention to enhance the in context learning ability of transformer.
- The paper aims at enhance inductive head bias of transformer by shifting the key&value vectors.
- This paper provides extensive analysis to show the effectiveness of the KV s... | Rebuttal 1:
Rebuttal: Thank you very much for your review. I hope the following response can address your concerns.
**Causal Conv**
Thank you very much for pointing this out. As demonstrated in our discussion, the shifting operation can be seen as a short convolution with kernel size 2.
(a) From an operational per... | Summary: This paper introduces KV shifting attention, a modification to the standard transformer attention mechanism that changes how keys and values are processed. By decoupling the temporal relationship between keys and values, the model can more efficiently learn induction heads - a critical mechanism for in-context... | Rebuttal 1:
Rebuttal: Thank you very much for your meticulous review. We hope our response can be helpful to you.
**More analysis**
(a) **What types of tasks or text patterns benefit most from the improved induction capabilities?** To be honest, it is difficult to exhaust what kind of pattern is more suitable, but w... | Summary: Based on the analysis of 2-layer attention, the authors found that 2-layer attention cannot effectively represent information flow from i+1 -> k -> j (for j>=i, k<i). Therefore, the authors proposed KV shifting attention to address this issue. The authors also conducted large-scale pre-training experiments to ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable review comments. I hope the following response can answer your concerns.
**Regarding expressive power**
Firstly, if I understand correctly, the "three" in the three-layer MLP you mentioned refers to the layer meaning of neuron nodes, which actually correspo... | null | null | null | null | null | null | null | null |
The Underlying Universal Statistical Structure of Natural Datasets | Accept (poster) | Summary: A model of natural dataset (at least natural images) is build based on a random matrix analysis and extensive data analysis. An interpretation is proposed for the power law spectrum of the covariance matrix of input features which generically shows up on natural data. In addition a further characterization of ... | Rebuttal 1:
Rebuttal: Dear Reviewer ZEdS,
Thank you for your careful review and positive appraisal of our work, tending towards acceptance. We’re glad you found the work interesting and well supported by empirical evidence.
Below, we address the remaining issues you raised, as well as questions and comments.
**Method... | Summary: The paper examines the empirical eigenvalues of Gram matrices of natural image datasets, showing that they follow a power law. It offers a simple model that reproduces this power law behavior using Gaussian data with long-range correlations. These results suggest that natural image Gram matrices can be approxi... | Rebuttal 1:
Rebuttal: Dear Reviewer kWZr,
Thank you for your careful review and positive appraisal of our work, deeming it acceptable at ICML. We’re glad you found the work interesting and well supported by empirical evidence.
Below, we address the remaining issues you raised, as well as questions and comments.
**Rel... | Summary: This paper aims to exploit Gaussian universality to generate synthetic data that accurately captures the statistics of natural data distributions. In particular, the paper proposes a synthetic model that reproduces the eigenvalue statistics of the covariance matrices of natural data. Since the synthetic data i... | Rebuttal 1:
Rebuttal: Dear Reviewer FARz,
We appreciate your thoughtful reading of our manuscript. Your main concern is the statement that natural data matrices behaving as Gaussian at sufficiently large dimensions is already known in the literature. We argue that this is **not the case** and we will attempt to expl... | Summary: The paper investigates the universal statistical structure underlying the covariance matrices (Gram matrices) of natural datasets. Leveraging Random Matrix Theory (RMT), it demonstrates that real-world datasets and correlated Gaussian datasets (CGDs) share universal spectral properties, including a characteris... | Rebuttal 1:
Rebuttal: Dear Reviewer LgA8,
Thank you for your positive reading of our manuscript, deeming our paper acceptable.
Below, we address your comments/questions:
**Essential References Not Discussed:**
We believe the list offered is likely a mistake, since all of these references are explicitly cited in o... | null | null | null | null | null | null |
KAN-AD: Time Series Anomaly Detection with Kolmogorov–Arnold Networks | Accept (poster) | Summary: The paper introduces KAN-AD, a novel approach to time series anomaly detection (TSAD) based on Kolmogorov–Arnold Networks (KANs). The motivation for this work stems from the limitations of existing TSAD methods, particularly those relying on forecasting models, which often overfit to local fluctuations and fai... | Rebuttal 1:
Rebuttal: # Response to reviewer UWPv
We thank the reviewer for the constructive suggestions and will further revise the manuscript accordingly. For **KAN-AD's Performance on MTS**, please refer to our response to reviewer HB8y.
**Clarification on the Claim of Local Smoothness in Normal Curves**: The smoot... | Summary: The paper discusses about the problem that most TSAD methods using forecasting models tend to overfit to local fluctuations, and reformulates time series modeling while approximating to smooth univariate functions. The paper adopted KAN backbone for TSAD, while replacing B-spine function with Fourier series fo... | Rebuttal 1:
Rebuttal: # Response to reviewer HB8y
**Synthetic Method of the TODS Dataset**: We used the synthetic TODS dataset from [1], which includes all five anomaly types with diverse durations and the non-trivial characteristics introduced by TODS. This dataset is publicly available in their repository.
**KAN-AD'... | Summary: This paper focuses on time series anomaly detection (TSAD), and proposes KAN-AD, which models "normal" behavior of time series using smooth functions. The paper addresses the shortcoming of existing methods that tend to overfit local variances in time series data. Proposed KAN-AD is a clever and novel approach... | Rebuttal 1:
Rebuttal: # Response to reviewer HZbR
We appreciate the constructive suggestions provided by the reviewer and will incorporate improvements in the subsequent version of the paper.
**Performance of KAN-AD on MTS**: We can reshape the original multivariate input `(batch, window, n_multivariate)` into `(batc... | Summary: This paper proposes a method for univariate time series anomaly detection. In particular, they aim to approximate the time series using smooth univariate functions. They build upon a method that uses Kolmogorov-Arnold Networks to approximate the time series, by replacing B-splines functions with Fourier series... | Rebuttal 1:
Rebuttal: # Response to reviewer GaXC
We appreciate the constructive comments provided by the reviewer and will incorporate improvements in the subsequent version of the paper.
**Comparative analysis of algorithm strengths**: Indeed, different methods are good at detecting specific types of anomalies. For... | null | null | null | null | null | null |
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models | Accept (poster) | Summary: The paper introduces *ContinualRAG*, a novel retrieval-augmented generation (RAG) framework designed to enhance large language models (LLMs) with a human-like long-term memory system for non-parametric continual learning. Building on the HippoRAG framework, ContinualRAG aims to address limitations in standard ... | Rebuttal 1:
Rebuttal: We are very thankful for the reviewer’s kind acknowledgment of our work as convincing, well-structured and broadly applicable across memory types. We will address the reviewer’s careful suggestions in the sections below.
## Statistical Significance Testing
> Why were no statistical tests reporte... | Summary: This paper proposes ContinualRAG that improves the performance of RAG on natural question answering and multi-hop reasoning benchmarks.
The method builds on the prior work, HippoRAG, which performs reasoning on a knowledge graph constructed at an offline phase. In the offline indexing phase, HippoRAG extracts... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for thoroughly reviewing our work and noting that our modifications are well-justified and bring strong improvements over all our baselines.
## ContinualRAG is a Continual Learning Method
> The paper is named “ContinualRAG”, however, the method does not seem t... | Summary: This paper presents a method to enhance traditional RAG models for large language models. The proposed approach is based on HippoRAG and introduces a combination of phrase nodes and passage nodes, inspired by how human memory represents and processes information at different granularities. Additionally, the me... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s detailed comments and questions, they will surely enhance the quality of our work. We are happy to know that the reviewer found our human memory inspired methodology well-motivated and our experimental settings meaningful, valid and sound.
## Non-Parametric... | Summary: **Main findings**:
The ability to continuously acquire, organize, and leverage knowledge is a fundamental aspect of human intelligence. To empower LLMs with this capability, retrieval-augmented generation (RAG) has emerged as a critical approach. Recent methods enhance vector embeddings by integrating struc... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for the time and effort dedicated to reviewing our work. We are delighted that they found our work clear, convincing, well-motivated, technically solid and empirically validated by impressive performance improvements. We address their suggestions and comments b... | null | null | null | null | null | null |
Generalized Smooth Bilevel Optimization with Nonconvex Lower-Level | Accept (poster) | Summary: The paper investigates bilevel optimization problems where the inner function is nonconvex, and both inner and outer functions satisfy a generalized smoothness assumption. To address this problem, the single-level constrained formulation of bilevel optimization is adopted, replacing the inner problem with the ... | Rebuttal 1:
Rebuttal: Thanks so much for your comments and suggestions. We response to your questions one by one as follows:
**Q1:** What is the meaning of stationary point in this context since the lower level problem is not assumed to be strongly convex?
**A1:** In our paper, we use the same definition of stationar... | Summary: This paper proposes a gradient-based first-order algorithm called PNGBiO for generalized-smooth nonconvex-weakly-concave bilevel optimization. The authors provide the convergence analysis and claim that this algorithm achieve a convergence rate of O(ϵ^(-4)) for finding an approximation stationary point. The au... | Rebuttal 1:
Rebuttal: **Q1**: The authors claim that the proposed algorithm solve the generalized-smooth bilevel optimization with nonconvex condition for both upper and lower level but only give an assumption that the lower-level objective function is weakly-convex. Did I miss something?
**A1**: Thanks for your comme... | Summary: This paper studies the generalized smooth bilevel optimization, where the upper-level objective is nonconvex with generalized smooth, and the lower-level objective is weakly convex and generalized smooth.
It proposes an effective Hessian/Jacobian-free penalty normalized gradient (PNGBiO) method to solve thes... | Rebuttal 1:
Rebuttal: **Q1**: This paper studied the generalized smooth bilevel optimization relying on the generalized smoothness introduced in [1]. The proposed PNGBiO method could apply the generalized smooth bilevel optimization with generalized smoothness introduced in [2] ?
**A1**: Thanks for your comment. We st... | Summary: This paper introduces an efficient Hessian/Jacobian-free Penalty Normalized Gradient (PNGBiO) method for solving bilevel optimization problems, where the upper-level objective is generalized smooth and nonconvex, while the lower-level objective is generalized smooth and weakly convex. Furthermore, the authors ... | Rebuttal 1:
Rebuttal: **Q1**: Using the same parameter $M$ to denote two different terms in the convergence analysis (at lines 240 and 266) could indeed be a typo or an oversight in notation.
**A1**: Thanks for your comment. There is a typo. We will correct it in the final version of our paper.
**Q2**: Since the nor... | null | null | null | null | null | null |
xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference | Accept (poster) | Summary: This work builds on the work by Beck et al. on language modeling with xLSTMs. It introduces several architecture modifications and tricks that support efficiency and training stability, and it introduces an (open sourced) pre-trained 7B parameter language model based on mLSTM. The paper contains a detailed des... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback. We appreciate that the reviewer finds that our claims are supported by evidence, that our experimental results are thorough and detailed and that our strong model is a valuable contribution to the open-source community.
### Attempt on Suggested I... | Summary: In this paper, the authors introduce a new 7B LLM xLSTM 7B. The model is built upon optimized xLSTM architecture to achieve better training stability and efficiency. Extensive experiments show that xLSTM 7B is memory and computation efficient compared to attention-based models and Mamba-based models, and achie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback.
We appreciate that the reviewer finds our paper well-written, easy to follow and that your claims are supported by evidence as well as that our experimental design is valid and reasonable.
### Architecture Illustrations
We agree with the reviewe... | Summary: This paper mainly scales XLSTM to 7B using some optimization technique
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: not many theoretical claims.
Experimental Designs Or Analyses: yes
Supplementary Material: yes, experimental parts
Relation To Broader Scientific Litera... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting our optimizations as a clear contribution and seeing strengths in our results in the trade offs we make, our good latency, and the interesting xLSTM framework.
### Discussion on the mLSTM cell
We agree with the reviewer that there is only a brief discussio... | null | null | null | null | null | null | null | null |
Oscillation-Reduced MXFP4 Training for Vision Transformers | Accept (poster) | Summary: This paper proposes two methods to train vision transformers with MXFP4-accelerated GEMMs. In the backward pass, the authors add stochastic rounding and scaling to achieve unbiased gradient estimates. In the forward pass, the authors add various EMA-based methods to avoid weight oscillation during quantization... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and the acknowledgment of our contributions. Below we respond to the questions.
> Question 1:
> … Are unbiased gradients necessary to get oscillation reduction to work, or is oscillation reduction necessary to get unbiased gradients to have an effect on... | Summary: The authors propose a MXF4 training scheme for Vision Transformers. Training at extremely low-bit such as 4-bit formats is challenging and prone to high accuracy loss, mainly due to weight oscillations in the forward pass as identified by the authors. The paper outlines two methods EMA Quantizer (Q-EMA) and Ad... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and the acknowledgment of our contributions. Below we respond to the questions.
> Broader Scientific Literature:
> The authors might enhance their context by explicitly citing some recent low-precision training surveys...
Thanks for the valuable adv... | Summary: The paper introduces TetraJet, a novel training method for Vision Transformers using the MXFP4 low-precision format, which is supported by Nvidia's Blackwell GPUs and offers significant speed improvements. The authors identify weight oscillation as a key issue causing accuracy degradation in MXFP4 training. To... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments. Below, we respond to the questions.
> Claims And Evidence & Theoretical Claims:
> Lack formal theoretical validation & Leaving some theoretical gaps
Thank you for the insightful comment. We agree that theoretical analysis is important. While our cu... | null | null | null | null | null | null | null | null |
Enhancing Treatment Effect Estimation via Active Learning: A Counterfactual Covering Perspective | Accept (poster) | Summary: This paper proposes a data-efficient method to construct a regression model for estimating the mean difference between the treatment and control responses. Specifically, the author upper-bounds the evaluation error and proposes two radius-based approaches to minimize the upper bound with a limited label budget... | Rebuttal 1:
Rebuttal: **Active Learning vs. Coreset Learning:** The problem setting we described in lines 114-127 (left column) is an active learning loop that consists of 5 steps, where in Step 4 we identify a subset from the completely unlabeled pool set $\mathcal{D}=\{(\mathbf{x}_ {i},t_ {i})\}^{N}_ {i=1}$ for the o... | Summary: This paper tackles causal effect estimation in active learning, where only features and treatments are known, but outcome labeling is costly and incomplete. To ensure balanced label expansion, it bounds generalization error using factual and counterfactual covering radius. Starting with a greedy approach, it d... | Rebuttal 1:
Rebuttal: **Q1:** For instance, when performing A/B tests to compare two software versions, the service provider needs to assign two distinct user groups to different versions (i.e., treatments). To understand which software version offers a better experience for a specific user profile (i.e., features), it... | Summary: This paper proposes an active learning (AL) approach tailored to enhance the treatment effect estimation from observational data, when labeling outcomes is costly. The authors introduce a theoretical formulation using the concepts of factual/counterfactual covering radii, to upper bound the (fundamentally inc... | Rebuttal 1:
Rebuttal: **Potential Failure Modes:** FCCM is designed to better handle the partially overlapped data for a quicker bound reduction while maintaining high coverage (Figure 3). As such, in scenarios where the two treatment groups have no overlapping regions (e.g., biased treatment assignment), the data acqu... | Summary: This paper tackles the problem of active learning for treatment effect estimation, where the task is to label a treated or untreated unit given a fixed budget. The authors take the core set approach and extend it by introducing a counterfactual covering radius along with the factual covering radius. An algorit... | Rebuttal 1:
Rebuttal: **Q1:** $\Gamma$ is a pseudo-operator defined in the "Input'' section of the Algorithm 1, i.e., $\Gamma=\arg\max\min d(\cdot,\cdot)$. We use $\Gamma$ as a shorthand for the distance-based radius defined underneath Eq. (8) (line 203 right column) to keep Algorithm 1 concise. Take line 6 of Algorith... | null | null | null | null | null | null |
Context-Informed Neural ODEs Unexpectedly Identify Broken Symmetries: Insights from the Poincaré–Hopf Theorem | Accept (poster) | Summary: The paper introduces Context-Informed Neural ODEs (CI-NODEs), a framework designed to learn dynamical systems exhibiting bifurcation behaviors, particularly symmetry-breaking bifurcations. The authors claim that CI-NODEs, despite being trained solely on pre-bifurcation, symmetric data, can predict post-bifurca... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and constructive feedback. Below, we address each comment carefully. Additional experimental results are available in the **README** at https://github.com/anonymous-account123/icml2025-7637 and will be thoroughly incorporated into our revised paper... | Summary: The paper finds that context-informed Neural Ordinary Differential Equations (NODEs) can identify symmetry-breaking bifurcations in dynamical systems (DS). By leveraging topological invariants like the Poincaré index and the Poincaré-Hopf theorem, the paper demonstrates conditions under which context-informed... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and constructive feedback. Below, we address each comment carefully. Additional experimental results are available in the **README** at https://github.com/anonymous-account123/icml2025-7637 and will be thoroughly incorporated into our revised paper... | Summary: The paper demonstrates that context-dependent Neural Ordinary Differential Equations can identify post-bifurcation behaviors, even when trained only on pre-bifurcation data. It then provides an interpretation for this phenomenon based on the Poincaré-Hopf theorem, and proposes a topological regularizer that mi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and constructive feedback. Below, we address each comment carefully. Additional experimental results are available in the **README** at https://github.com/anonymous-account123/icml2025-7637 and will be thoroughly incorporated into our revised paper... | Summary: This paper explores the use of context-informed NODEs to identify symmetry-breaking bifurcations in dynamical systems without relying on physics-based training data. The authors demonstrate that NODEs trained solely on symmetric (pre-bifurcation) data can predict post-bifurcation behaviors in a "zero-shot" man... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful and constructive feedback. Below, we address each comment carefully. Additional experimental results are available in the **README** at https://github.com/anonymous-account123/icml2025-7637 and will be thoroughly incorporated into our revised paper... | null | null | null | null | null | null |
Sundial: A Family of Highly Capable Time Series Foundation Models | Accept (oral) | Summary: The paper presents a collection of foundation models for time series. To this end, the authors proposed a loss called TimeFlow for predicting the distribution next-patch, enabling Transformers to be pre-trained without the need for discrete tokenization. It is argued that this loss function helps in preventing... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer 4SCU for providing a valuable review.
> **Q1**: Include the relevant citation (Li et al. 2024).
We referred to it in Section 5.3. In this Section, we provided the comparison using different training objectives: MSE Loss, Diffusion Loss (Li et al., 2024), and TimeFlow Loss... | Summary: The paper introduces Sundial, a novel family of time series foundation models that address fundamental challenges in time series forecasting through a native, flexible, and scalable approach. The work's primary innovation is the proposed TimeFlow Loss, an optimization objective based on flow-matching that enab... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer gDT6 for providing a detailed review and recognizing our contributions.
> **Q1**: Quantitative evaluation of prediction diversity.
Thanks for your suggestion. We extend the ablation study of Table 3, where we provide probabilistic metrics CRPS to evaluate the diversity. N... | Summary: This paper introduces Sundial, a family of native, flexible, and scalable time series foundation models. It proposes a TimeFlow Loss based on flow-matching for model training, which can generate multiple probable predictions. It also proposes some crucial adaptations of Transformers and the TimeBench with 1 tr... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer VkQC for providing thorough insightful comments.
> **Q1**: Explanations about different tokenization ways and modeling techniques.
Thanks for your suggestion, we provide a comparison to enhance the clarity:
| Tokenization | Meanings| Advantages | Disadvantages |
|-|-|-|-... | null | null | null | null | null | null | null | null |
Offline Opponent Modeling with Truncated Q-driven Instant Policy Refinement | Accept (poster) | Summary: This paper proposes to learn a horizon-truncated incontext action-value function and a policy refinement mechanism to tackle the offline opponent modeling problem, espically when the training dataset is suboptimal. This paper has analyzed the rationale of Truncated Q from the perspective of No Maximization Bi... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. In response, we offer the following clarifications and hope our explanations address your concerns and strengthen our work.
---
> **(A) Theoretical Claims:** 1. What's the definition of $\breve{Q} _\mathsf{h}$ in Equation 2.
>
$\mathsf{h}$ denotes the rand... | Summary: The paper proposes an offline opponent modeling approach that enhances the consistency of Q-functions by truncating the horizon length during Q-learning, incorporates in-context learning to mitigate distribution shift caused by sub-optimality in offline datasets, and employs test-time policy refinement to furt... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We'd like to offer the following clarifications and hope our responses address your concerns and strengthen our work.
---
> **(A) Weaknesses:** 1. The authors use a MEP approach to generate 4 opponents …
**Questions For Authors:** 4. … How are 20 policies sa... | Summary: This work aims to model opponent in multi-agent learning scenario from offline data. They argue that the offline data may be suboptimal and lead to suboptimal policies. To address this, they propose to learn a horizon-truncated in-context Q-function and whether to perform policy refinement. Truncated Q-driven ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. In response to your comments, we would like to make the following clarifications and feedback. We hope our explanations and analyses can eliminate concerns and make you find our work stronger.
---
> **(A) Other Comments Or Suggestions:** The paper ... | Summary: This paper introduces a framework called Truncated Q-driven Instant Policy Refinement (TIPR) to improve Offline Opponent Modeling (OOM) algorithms trained on suboptimal datasets where the self-agent may not always select best-response (BR) actions to its opponents. Unlike prior OOM methods that assume optimal ... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We'd like to clarify a few points in response, hoping our explanations address your concerns and strengthen our work.
---
> **(A) Theoretical Claims: …** the empirical risk bound in eq-4 assumes a specific distribution of Q-learning errors, which depends on well-beha... | null | null | null | null | null | null |
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed | Accept (poster) | Summary: This paper studies high probability convergence rate of clip-AdamNorm and clip-AdaGradNorm under heavy-tailed noise. The authors show that Adam/AdaGrad fails to get convergence rate with $log(1/\delta)$ dependence if the noise is heavy-tailed. Then the authors show that gradient-clipping can fix this issue for... | Rebuttal 1:
Rebuttal: We thank the reviewer for the very positive evaluation of our paper and constructive feedback.
>1:**Analysis of Clip-Adam with $\beta_1 > 0$**
A: We revisited our proof and generalized it to the case of $\beta_1 > 0$. The sketch of the proof with the key derivation is provided below. We promise ... | Summary: This paper provides a loss function that AdaGrad and Adam have bad high probability convergence when the noise is heavy-tailed. Then they show a desirable convergence rate for AdaGrad-Norm and Adam-Norm with clipping gradient to fix the heavy-tailed noise issue. They conduct experiments to show that clipping c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback that helped improve our paper. The requested numerical experiments can be found here: https://anonymous.4open.science/r/Clip-Adam-Norm-and-Clip-AdaGrad-Norm-1E8A/ (folder "rebuttal").
>1: **Algorithm 1.**
A: We will add "-norm" to method names in A... | Summary: This work examines adaptive optimization methods, specifically variants of Adam and AdaGrad, in settings with heavy-tailed noise. The authors establish that for Adam and AdaGrad with momentum, achieving a polylogarithmic dependence on the confidence level is impossible. In contrast, they show that the clipped ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback that helped to improve our paper.
>1:**Time-dependent noise.**
A: Thank you for spotting this. We will rewrite Assumption 1.1 to explicitly highlight that we allow time-dependent noise. Such assumptions are relatively standard for stochastic o... | Summary: This paper suggests that clipping enables high-probability convergence (with polylogarithmic dependence on the confidence level δ) for Adam-norm/AdaGrad-norm under heavy-tailed noise. In contrast, without clipping, Adam/AdaGrad has inverse-power dependence.
The authors provide some numerical results to support... | Rebuttal 1:
Rebuttal: Thank you for your time and positive feedback. We also thank you for your useful comments, which we have addressed below.
>1:**It’s not the specific settings but rather the scale that I’m concerned with. I believe the experiments can be much more comprehensive to claim a solid support of the theo... | null | null | null | null | null | null |
HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training | Accept (poster) | Summary: This paper presents a framework for geo-distributed LLM training named HALoS. To reduce the staleness effect, HALoS introduces local parameter server (LPS) for workers within each region, and periodically syncs LPS with global parameter server (GPS). This paper also introduces a convergence analysis for this a... | Rebuttal 1:
Rebuttal: # Clarification on Questions
**(Q1)** We assign the same mini-batch size per worker. For heterogeneous clusters, we avoid ineffective convergence from imbalanced numbers of workers per LPS by adopting **a consistent grouping strategy** that assigns an equal number of workers to each LPS. For examp... | Summary: This paper presents HALoS, a hierarchical asynchronous optimization framework for training large language models (LLMs) across geographically distributed hardware. HALoS addresses communication bottlenecks by using local parameter servers (LPSs) within each region and a global parameter server (GPS) that merge... | Rebuttal 1:
Rebuttal: # [W1] Refining Introduction and Related Work
We appreciate the suggestion to enhance the clarity and readability of our paper. We designed the introduction to provide sufficient motivation and context for HALoS by outlining both the challenges of geo-distributed LLM training and relevant prior wo... | Summary: The paper presents HALoS, an optimization framework designed to enhance cross-region training of large language models (LLMs). To address the challenges of communication costs and imbalanced hardware utilization, HALoS employs a hierarchical architecture with local parameter servers within each region and a gl... | Rebuttal 1:
Rebuttal: # [W1] HALoS without i.i.d. assumption
We appreciate the reviewer’s insightful question regarding the i.i.d. assumption. While our theoretical analysis in Section 4.2 follows standard practice by assuming i.i.d. data for analytical clarity, **HALoS does not rely on this assumption in practice.** T... | null | null | null | null | null | null | null | null |
LETS Forecast: Learning Embedology for Time Series Forecasting | Accept (poster) | Summary: In this paper, the authors introduce DeepEDM, a framework that extends Empirical Dynamical Modeling (EDM) to learn complex latent non-linear dynamics from observed time series for improved forecasting. Building upon Takens’ theorem, DeepEDM first constructs time-delayed embeddings of the input data and then pr... | Rebuttal 1:
Rebuttal: Thanks for your detailed feedback. We’re pleased that you recognize the novelty of combining Empirical Dynamical Modeling with deep learning and the strong performance of DeepEDM, especially in noisy settings. We also appreciate your acknowledgment of our connection to the Attention mechanism and ... | Summary: This paper introduces DeepEDM, a framework that integrates nonlinear dynamical systems modeling with deep neural networks for time series forecasting. Built on empirical dynamic modeling (EDM) and Takens' theorem, DeepEDM employs time-delayed embeddings in a latent space and uses kernel regression to approxima... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and recognition of our paper’s clarity, structure, and experimental design. We appreciate the acknowledgment of our contribution to nonlinear dynamical systems through EDM-integrated deep learning. Below, we address your suggestions and will incorporate these r... | Summary: The paper resorts to first principles, to examine the usefulness of using embedology (as in Takens' embedding theorem) in conjunction with neural networks. This is important and has been missing in the literature.
Claims And Evidence: DeepEDM claims three key advantages.
(1) By learning a latent space of the... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and recognition of our theoretical contributions. We appreciate your acknowledgment that embedology, despite its foundational role, has been largely overlooked and that our work helps bridge this gap by integrating Takens' embedding theorem with neural networks... | Summary: This paper introduces a new framework, DeepEDm, for time series forecasting, inspired by studies in Empirical Dynamic Modeling (EDM). The approach is fundamentally based on Taken's theorem, which states that a dynamic system can be reconstructed using a delay embedding of the series in phase space.
In practic... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review. We now address your suggestions. Our response will be integrated into the final version.
### 1. Clarification on Generalization Statement
This was meant to contrast DeepEDM and EDM w. simplex: EDM trains a separate model for each time series, wh... | null | null | null | null | null | null |
Power Mean Estimation in Stochastic Continuous Monte-Carlo Tree Search | Accept (poster) | Summary: The submission introduces a new MCTS algorithm for continuous and stochastic MDPS. The method combines power mean backups (known to be more stable than simple averaging) with polynomial exploration bonuses (known to lead to improved converged compared to logarithmic exploration) and an HOO-like partitioning sc... | Rebuttal 1:
Rebuttal: Thank you for your positive review and for suggesting additional related work we hadn't cited. We appreciate your thorough analysis of our theoretical claims. We address each of your concerns in detail and kindly ask you to consider updating your scores after reading the rebuttal.
### Including A... | Summary: The paper considers MCTS for continuous action space and non-stochastic environments. The authors propose to use a power mean operator for value estimates. They also propose a polynomial exploration bonus. They show convergence results for stochastic environments that matches previous results on deterministic ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review of our submission. We appreciate your recognition of our theoretical contributions and have addressed your concerns with additional justification and expanded experiments.
## Power Mean Estimation: Detailed Justification
- The power mean effectively balances ... | Summary: This paper introduces Stochastic-Power-HOOT, a novel Monte-Carlo Tree Search (MCTS) algorithm designed for continuous, stochastic MDPs. The authors propose integrating a power mean as a value backup operator along with a polynomial exploration bonus to address the related challenges. The paper provide theoreti... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and recognition of our theoretical contributions. We address each of your concerns in detail and kindly invite you to consider updating your scores
### Complex Environments
We've expanded our experiments to include high dimensional MuJoCo environments with ad... | Summary: This paper introduces Stochastic-Power-HOOT, an extension of HOOT designed to handle stochastic and continuous-action Markov Decision Processes (MDPs), where prior methods primarily focused on deterministic settings. The core contributions include:
- Power mean backup operator to mitigate non-stationary rewar... | Rebuttal 1:
Rebuttal: | Algorithm | Humanoid-v0 | Hopper-v0 |
|-----------|---------------------|-------------------|
| UCT (baseline) | -136.98 ± 44.84 (1.0x) | 5216.93 ± 179.64 (1.0x) |
| POLY-HOOT (p=1) | -44.40 ± 3.33 (3.1x) | 13230.66 ± 2844.33 (2.5x) |
| **Stochastic-Power-HOOT** | **-44.12 ± 6.22 (3.1x)(p=2)** |... | Summary: In the setting of continuous state/action MCTS, this work proposes replacing the empirical mean node value estimate with a power mean-based estimate. They also propose a tree action selection bonus based on this power mean. They provide convergence proofs for this method in the setting of stochastic MDPs, and ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We address each of your concerns in detail and kindly ask you to consider updating your scores after reading the rebuttal.
## Experimental Design
**Higher-dimensional tasks:** We added results on Humanoid (17D action, 376D state) and Hopper (3D action) with ad... | null | null | null | null |
Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster | Accept (poster) | Summary: Establishes rates for Armijo line search in several settings.
Claims And Evidence: There are some interesting results in this paper, and I like the expansion of our understanding of the (L0, L1)-smoothness condition and it's generalizations.
I object to the use of stochastic to refer to analysis under interp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and address their concerns below.
> *(1) My major concern with this paper is that it doesn't have a related work section, and seems to only cite very old papers concerning line searches, as well as a nearly 20 year old text book...Please include t... | Summary: The paper analyzes functions where the local smoothness constant is given by \\( L(\\theta) = L_0 + L_1 f(\\theta) \\), which is satisfied by common objectives like logistic regression or regression problems with generalized linear models. The authors prove that Gradient Descent with Armijo Line-Search (GD-LS)... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and address their concerns below.
> *(1) The targeted optimization problem in this paper is limited in terms of application perspective. The authors primarily focus on logistic regression and generalized linear models. Although the authors showed ... | Summary: This paper studies gradient descent using Armijo line search to choose the stepsize. They prove convergence rates for the algorithm under a non-uniform smoothness condition, and specialize these results to logistic regression, softmax policy gradient for multi-armed bandit problems, and GLMs with the logistic ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and address their concerns below. **For all unaddressed comments, we agree with the reviewer's suggestion and will make the corresponding change.**
> *(1) Lower-bounds for $\texttt{GD(1/L)}$*
For logistic regression, Theorem 3 in Wu et al, 2024 s... | Summary: This paper investigates the effectiveness of the Armijo line-search (Armijo-LS) method for step-size selection in gradient descent (GD) algorithms. The authors introduce a class of functions that satisfy a non-uniform smoothness condition and show that GD with Armijo-LS (GD-LS) can adapt to local smoothness, l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and address their concerns below.
> *(1) Independence of Assumptions*
Assumptions 3 and 5 do indeed imply Assumption 2(b). The reason we did not use Assumption 5 as our main assumption is because Assumption 5 cannot be satisfied for logistic regr... | null | null | null | null | null | null |
Learning to Stop: Deep Learning for Mean Field Optimal Stopping | Accept (poster) | Summary: The authors consider mean field control problems where agents must perform optimal stopping, i.e., taking an action that stops the evolution of their state. The cooperative optimal stopping problem is theoretically reduced into a standard mean field control problem by extending the state with information of wh... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive suggestions to improve our paper. We provide detailed responses to each of your questions below.
**(Theoretical Claims)** We would like to thank the reviewer for spotting a typo in the proof of Lemma A.1. We corrected the proof by m... | Summary: The goal of this paper is to extend the classical optimal stopping problem to multi-agent systems. While existing theoretical results primarily address continuous-time settings—such as those arising in options pricing—this work specifically studies discrete-time and discrete-state scenarios. The central approa... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the helpful suggestions to improve our work, especially for raising the connection to the options framework in reinforcement learning. While we agree that this seems connected to MFOS in spirit, we believe that we cannot make one fit in the other. We list specif... | Summary: The paper introduces a mean field optimal stopping (MFOS) framework as an approximation to multi-agent optimal stopping (MAOS) problems where many agents must decide when to stop a stochastic process. The authors establish a theoretical foundation for MFOS, proving that solving the infinite-agent (mean field) ... | Rebuttal 1:
Rebuttal: First of all, we would like to sincerely thank the reviewer for their detailed review, which highlights our contributions and originality, and for the constructive suggestions to improve our manuscript.
**(Restricted to Finite State Mean-Field)**. We agree with the reviewer that focusing on a f... | null | null | null | null | null | null | null | null |
Accelerating PDE-Constrained Optimization by the Derivative of Neural Operators | Accept (poster) | Summary: The paper improve on the reference neural operator (RNO) for PDE-constrained optimization (PDECO) by (1) using the full trajectory data of PDECO and the sensitivity loss (2) introducing the virtual Fourier layer (3) using numerical solution to correct the neural operator solution.
Claims And Evidence: The cl... | Rebuttal 1:
Rebuttal: Thank you for your review with carefulness and details, and it is highly valuable and helpful to us!
1. **Claims and Evidence**
Nice catch on the mismatch of objective! As you pointed out, the objective of Microreactor is to minimize $J = -\int…$ in eq. (17) in appendix C.2. This objective is e... | Summary: The authors propose a neural-operator based approach for solving PDE-constrained optimization problems. While this approach is not new in the literature, the authors come up with several innovations to improve existing approaches: (i) data-driven training by using trajectories generated by traditional optimiza... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, your feedback is in-detail and comprehensive. Among your comments we notice that you particularly expressed “having a hard time following virtual-Fourier (VF) layers and distinguishing VF from FNO”. We would like to clarify this point first since it lies in the c... | Summary: This paper proposed a novel framework to enhance PDE-constrained optimization (PDECO) with neural operators, addressing data efficiency and robustness. Key innovations include data-driven training, a Virtual-Fourier layer for improved derivative learning, and a hybrid optimization approach integrating neural o... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, your summarization is precise and your suggestion on additional references are well received. Particularly, we thank you for suggesting the 2nd work that we'd like to discuss the distinction with our work. The first distinction is that their work is targeting tim... | Summary: Authors propose a general architecture-agnostic framework that can be used for PDE-constrained optimization and a Virtual Fourier Layer suitable for data on irregular grids.
The framework consists of three essential parts: (i) a particular structure of inputs and outputs for selected neural networks, (ii) a w... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper in such depth and being very informative, especially on reference. It is highly valuable to us!
1. **Experimental Designs Or Analyses**
We respectfully disagree that Virtual Fourier (VF) Layer is least important. It reflects our thoughts on designing architectur... | null | null | null | null | null | null |
Accelerating Quantum Reinforcement Learning with a Quantum Natural Policy Gradient Based Approach | Accept (poster) | Summary: This paper presents a method for quantum based NGD for RL that improves the asymptotic scaling of the sample complexity. Building on previous work that analyze quantum mean estimation and variance reduction, this paper shows proves its algorithms achieves O(eps^-1.5) sample complexity (over the classical -2).
... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and insightful comments. Below we clarify and address the points raised in your review:
## **Regarding Novelty:**
We appreciate the reviewer’s concern regarding the novelty of our work. To clarify, our paper is indeed the first in quantum parameterized re... | Summary: In this paper, the authors introduce a novel algorithm called Quantum Natural Policy Gradient (QNPG). In QNPG the classical Natural Policy Gradient (NPG) estimators are replaced with a deterministic gradient estimation approach. Theoretically they show that QNPG achieves a lower sample complexity for queries t... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and valuable insights. We address your primary concerns clearly below:
## **Clarification on the "Model-Free" Nature of QNPG:**
We appreciate the insightful discussion about whether our Quantum Natural Policy Gradient (QNPG) algorithm should be considered "mo... | Summary: The paper introduces a novel Quantum Natural Policy Gradient (QNPG) algorithm aimed at accelerating reinforcement learning (RL) in quantum computing environments. The authors introduce QNPG, a quantum-compatible variant of the classical Natural Policy Gradient (NPG) algorithm. The key innovation involves repla... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful comments and valuable feedback. We address the main points and questions raised as follows:
## **Response to Weakness 1 and Questions 1-3:**
**Why Quantum Computing Accelerates Mean Estimation (Lemma 1)**:
Quantum computing fundamentally accelerates mean... | Summary: This paper introduces a Quantum Natural Policy Gradient (QNPG) algorithm aimed at accelerating quantum reinforcement learning. The key idea is to replace the classical random sampling in natural policy gradient estimation with a deterministic gradient estimation method that can be integrated into quantum syste... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and constructive comments. We address your concerns and questions clearly below:
## **Response to Weaknesses 1, 4, and Question 1:**
**Practicality of Quantum Oracle Implementations and Experimental Validation**:
We acknowledge the reviewer’s valid concerns... | null | null | null | null | null | null |
Preference Adaptive and Sequential Text-to-Image Generation | Accept (poster) | Summary: The paper proposes a novel method for text-to-image (T2I) generation that adapts to user preferences over the course of multiple turns. The problem is framed as a Markov decision process (MDP) where the initial state is some prompt $p_0$ provided by the user, each subsequent state is given by the history of in... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s helpful feedback, as well as their recognition of the significance and originality of our work and the value of sharing our distinctive dataset. Below, we respond to the reviewer’s comments.
**A: Model weights and training code release**
We are currently reviewing op... | Summary: This paper introduces PASTA, a reinforcement learning (RL) agent designed for preference-aligned, sequential text-to-image (T2I) prompt expansion. The framework aims to enhance T2I generation by formulating multi-turn interactions as a sequential decision-making problem, leveraging LMMs (Gemma) and image diffu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and hope that the following response addresses the reviewer’s concerns.
**A: "The lack of experiments with other SOTA T2I generators.."**
**Response:** To address this, we are running additional test-time experiments evaluating models our agent wa... | Summary: This paper introduces PASTA, a RL framework for interactive text-to-image (T2I) generation.
This method enables multi-turn collaboration between models and users to refine prompts/images iteratively to align the preferences of users.
A novel dataset of sequential user interactions is proposed and a user simul... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer’s constructive feedback and for pointing out our dataset contribution as the first T2I dataset for multi-turn interaction setting. Bellow we address the concerns raised by the reviewer:
**Comment 1: "This paper does not mention the scale of user annotations."**
*... | Summary: This paper introduces PASTA, a novel reinforcement learning framework for interactive text-to-image generation. It addresses the challenge of capturing precise user intent through iterative prompt expansion. The core ideas involve using a large multimodal language model (LMM) for prompt candidate generation, a... | Rebuttal 1:
Rebuttal: We appreciate your positive feedback on the novelty of our approach and our dataset contribution for the community. Bellow we address the concerns raised by the reviewer:
**Comment 1: "...the performance gains from the user simulation require further scrutiny."**
**Response:** The user simulator... | null | null | null | null | null | null |
Expected Variational Inequalities | Accept (oral) | Summary: This paper considers a relaxation of the variational inequality problem (VIP), the $\phi$-Expected Variational Inequality problem (EVI) (see Def. 1.2). This definition relaxes the 'classical' definition of the VIP in three ways: (1) the objective is to find a distribution $\mu$ such that the inequality holds i... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and service.
*"To the best of my understanding, if $\Phi$ contains all functions [...] EVIs would correspond to VIs if $\mu$ is restricted to Dirac measures."*
We clarify that this equivalence holds without any restriction on $\mu$; it doesn’t have to b... | Summary: The authors relax VI to a new notion of EVI. They then take a game-theoretic approach to design polynomial-time approximation algorithms for EVIs.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes.
Experimental Designs Or Analyses: N/A
Supplementary Material: No.
Rel... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and service.
*“It would be helpful if the authors could compare the strength of VI to EVI in terms of modeling power. For example, are there still many VI problems for which EVI suffices? Furthermore, are there any problems where EVI is actually more natur... | Summary: The paper studies the Variational Inequality (VI) problem, a well-known problem used in various optimisation problems, often in settings where equilibria need to be computed. The general problem is intractable, so there is a wide literature identifying subclasses of the VI problem for which it is tractable.
T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and service.
We are happy to adjust the writing style and follow any other suggestions for the revision. Regarding the bibliography, we follow the convention that page numbers are omitted for conference publications, which explains why some references hav... | null | null | null | null | null | null | null | null |
From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information? | Accept (poster) | Summary: This paper introduces AR-Bench, a benchmark designed to test if large language models can actively gather missing information by asking the right questions. The benchmark includes tasks like detective cases, lateral thinking puzzles, and a number-guessing game, pushing models into multi-turn interactions rathe... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. To solve these concerns and questions, we provide point-to-point responses as follows.
> Q1. The reliability and necessity of using LLM as a judge, and evaluating the judge on Turtlebenchmark.
**Reply:** Here, we (1) explain why we use LLM judges instead of huma... | Summary: The paper proposes AR-Bench, a new benchmark to evaluate active reasoning abilities of large language models (LLMs) – i.e. their capacity to solve problems with incomplete initial information by asking questions. This contrasts with passive reasoning, where the model is given all necessary information up front... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. To solve these concerns and questions, we provide point-to-point responses as follows.
> Q1. The benchmark’s novelty and consistency with real-world scenarios.
**Reply:** We answer this question from three aspects.
**(1) Reality.** Real-world active reasoning c... | Summary: The paper addresses a significant research gap in evaluating Large Language Models (LLMs) for active reasoning, where models must actively query external sources due to incomplete information, rather than passively reasoning from complete data. The authors propose AR-Bench, featuring three active reasoning tas... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. To solve these concerns and questions, we provide point-to-point responses as follows.
> Q1. The selection of language models to compare.
**Reply:** We provide a two-fold answer as follows.
**(1) Considering the huge expense of computing and API calls, we evalu... | null | null | null | null | null | null | null | null |
A Near Linear Query Lower Bound for Submodular Maximization | Accept (poster) | Summary: The authors present query lower bounds for the problem of maximizing a monotone submodular function under a cardinality constraint.
Specifically, they improve the already existing lower bound of $\Omega(n/k)$ to $\tilde{\Omega}(n)$.
The bound holds for even estimating the value of the optimal set.
They also s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback, we will incorporate your suggestions into our paper. | Summary: They give a lower bound for monotone submodular maximization under cardinality constraint, showing any algorithm achieving a constant approximation factor requires $\Omega(n)$ query complexity.
They also provide an algorithm which estimates the maximum value when the function is additive using $\tilde{O}(n/k)... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback. We will incorporate your suggestions into our paper and expand the discussion on the motivation behind our algorithm for additive functions. | Summary: The paper studies the submodular maximization problem under cardinality constraint $k$. They prove a new lower bound on the required query complexity for achieving a constant factor approximation ratio, which improves upon established results when $\text{polylog}{(n)} \le k \le \frac{n}{\text{polylog}{(n)}}$. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful feedback and will incorporate your suggestions into our paper. | Summary: This paper studies the query complexity for monotone submodular maximization over cardinality constraint. The authors provide a nearly tight query lower bound $\tilde{\Omega}(n)$ for obtaining any constant factor approximation for any $k = o(n)$, which improves over the existing bound of $\Omega(n / k)$. This ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and will incorporate your suggestions into our paper.
> Are there any existing algorithms for estimating the optimal value of a monotone modular function using sublinear queries?
To the best of our knowledge, no such algorithms currently exist.... | null | null | null | null | null | null |
FreeMesh: Boosting Mesh Generation with Coordinates Merging | Accept (poster) | Summary: This paper propose a plug-and-play tokenize strategy for auto-regressive mesh generation. This strategy learns a BPE tokenzier from discretized mesh coordinate sequences to merge multiple coordinates into one token. This paper also propose the Per-Token-Mesh-Entropy to evaluate how hard it could be to learn to... | Rebuttal 1:
Rebuttal: ## 1. RMC Algorithm Clarification
**Reviewer Concern**: Handling groups with fewer than 9 coordinates (e.g., 7) was unclear.
**Response**:
For sequences with length < 9 (e.g., 7):
1. **Truncation**: Reduce to the largest multiple of 3 (e.g., 6).
2. **Permutation**: Reorder coordinates fr... | Summary: This paper introduces (1) Per-Token-Mesh-Entropy (PTME), a novel metric for evaluating mesh tokenizers without requiring training, and (2) "Rearrange & Merge Coordinates" (RMC) approach that improves existing tokenizers by rearranging and merging frequently occurring coordinate patterns. Experiments conducted ... | Rebuttal 1:
Rebuttal: ## 1. Benchmarking with BPT Method
**Reviewer Concern**: Missing comparison with BPT for point-cloud conditioned generation.
**Response**:
We have added quantitative and qualitative comparisons with BPT under identical settings:
| Method | Compress Ratio ↓ | HD ↓ | CD ↓ | Boundary E... | Summary: Core contributions are a new metric and a new approach to mesh tokenization. The new approach to tokenization uses BPE style compression, compress the tokenized representation of a mesh for the purposes of autoregressive mesh generation, getting additional compression by utilizing the permutation invariance of... | Rebuttal 1:
Rebuttal: ## 1. PTME-CD Correlation Analysis
**Reviewer Concern**: Need rigorous correlation between PTME and generation metrics.
**Response**:
For EDR+RMC, we calculated the Pearson correlation between PTME and Chamfer Distance (CD) under varying vocabulary sizes:
- **Pearson r = 0.965** , indicat... | Summary: The manuscript adapts subword tokenization techniques from natural language processing to compress mesh coordinate sequences, proposing the Rearrange & Merge Coordinates method to achieve higher mesh encoding efficiency while being easily integrated into existing mesh generation frameworks. Additionally, the m... | Rebuttal 1:
Rebuttal: ## 1. PTME vs Perplexity (PPL)
**Reviewer Concern**: Lack of theoretical validation for PTME compared to metrics like PPL.
**Response**:
1. **Fundamental Difference**:
- PPL requires model training and correlates poorly with final generation quality in our task.
- Empirical observ... | null | null | null | null | null | null |
A Novel Characterization of the Population Area Under the Risk Coverage Curve (AURC) and Rates of Finite Sample Estimators | Accept (poster) | Summary: This paper concerns evaluating the performance of *selective classifiers*, where the classifier has the option of abstaining from making a prediction when the confidence is low. For such classifiers, the Area Under the Risk Coverage curve (AURC) has been a commonly used (population) metric for evaluating their... | Rebuttal 1:
Rebuttal: ***Response to Reviewer BrTV:***
**Q1:** See general response from "Notably, the Monte Carlo estimator using $\hat{\alpha}_{i}$...from a theoretical perspective."
**Q2:** Take Fig. 3 as an example. When the confidence intervals are wide, it is typically due to the estimator being evaluated with ... | Summary: This work addresses estimation of AURC risk aware classification by Selective Classifiers (SC).
It considers the theoretical properties of a pre existing AURC estimators, specifically SELE and proposes several new population based estimators with improved convergence properties.
Furthermore, they conduct emp... | Rebuttal 1:
Rebuttal: ***Response to Reviewer 3mMn:***
**Claims and Evidence:**
We largely agree that our analysis focuses on the two proposed Monte Carlo estimators. In fact, one of them is equivalent to the widely used plug-in estimator, and our results confirm the effectiveness of this plug-in estimator from a th... | Summary: Introduces population Area Under the Risk-Coverage Curve (AURC) for selective classifiers. Statistical properties of these estimators are analyzed and evaluated on CIFAR datasets with VGG models.
Claims And Evidence: Needs more evidence evaluating AURC optimization (convergence, computational difficulty, etc)... | Rebuttal 1:
Rebuttal: ***Response to Reviewer cuHV: (continued): Please refer to the initial part of this response above.***
**Essential Reference Not Discussed:**
We agree that learning with rejection is related to AURC in relation to selective classifiers, where the model includes a reject option. However, this li... | Summary: This paper proposes a new approach for learning classifiers for selective classification tasks where classifiers need not only make predictions, but also decide if it wants make a prediction or to abstain. It reformulates the area under the risk coverage curve (AURC) as an optimization objective during trainin... | Rebuttal 1:
Rebuttal: ***General response:***
We thank all reviewers for the valuable feedback. We hope our response has addressed all the concerns.
**Supplementary Material:**
We indeed included the appendix as supplementary material; however, while two reviewers did not see it, the other two did and were able to... | null | null | null | null | null | null |
LangDAug: Langevin Data Augmentation for Multi-Source Domain Generalization in Medical Image Segmentation | Accept (poster) | Summary: This paper presents LangDAug, a novel data augmentation method designed to address multi-source domain generalization challenges in medical image segmentation. Leveraging Langevin Dynamics (LD) and Energy-Based Models (EBMs), LangDAug generates intermediate samples bridging source domains to enhance model gene... | Rebuttal 1:
Rebuttal: Thanks for your comments. We have tried our best to answer your concerns. We will be happy to engage in follow-up discussion if you have further questions.
## Computational and Storage Requirements
Thanks for this comment. We acknowledged this in our limitations, our method increases storage and ... | Summary: The authors propose LangDAug, a Langevin Data Augmentation technique to improve multi-source domain generalization in medical image segmentation tasks. The core idea is to train Energy-Based Models (EBMs) via contrastive divergence to model the transitions between pairs of source domains and use Langevin dynam... | Rebuttal 1:
Rebuttal: Thank you for your appreciation and in-depth review. We have tried our best to answer your concerns. We will include these in final version of the paper. Please let us know if you have any other questions.
### Computational Cost Analyses
We acknowledged the increased computational cost of LangDAug... | Summary: This paper proposes a data augmentation method via Langevin dynamics with theoretical analyses. They also conduct experiments to show its usefulness.
Claims And Evidence: In Line 019-023, "DA methods, which enrich model representations through synthetic samples, have shown comparable or superior performance t... | Rebuttal 1:
Rebuttal: Thank you for your comments. We have tried our best to address your concerns by adding more baseline comparisons with your suggest methods. We will include these in the final version of the manuscript. We will be happy to answer any follow-up questions you might have.
## Comparison with more Base... | null | null | null | null | null | null | null | null |
On the Diversity of Adversarial Ensemble Learning | Accept (poster) | Summary: This paper investigates the role of diversity in adversarial ensemble learning, addressing two key questions: how to define diversity formally in adversarial scenario and how diversity correlates. To address this questions, the paper introduces a first-order approximation and proposes a novel diversity decompo... | Rebuttal 1:
Rebuttal: [Q1] It would benefit to examine the performance of the authors’ proposed method with Expectation over Transformation (EOT) and Backward Pass Differentiable Approximation (BPDA).
[A1] We will clarify that this work does not consider the EOT and BPDA attacks, because the EOT attack is designed for... | Summary: This work explores the role of diversity in adversarial ensemble learning, focusing on its definition and impact on algorithmic performance. The authors demonstrate that precisely calculating diversity is NP-Hard, distinguishing it from traditional diversity analysis. They introduce the first diversity decompo... | Rebuttal 1:
Rebuttal: [Q1] This work proposes the diversity with the first-order approximation, which is somewhat limited. The neural network may not exhibit strong linearity in local regions.
[A1] We will clarify that the first-order approximation is motivated from previous first-order approximation methods for robus... | Summary: This paper addresses the critical role of diversity in adversarial ensemble learning, demonstrating that precisely calculating diversity for neural networks under adversarial perturbations is NP-hard due to structural and predictive interdependencies. By decomposing adversarial ensemble loss into four componen... | Rebuttal 1:
Rebuttal: [Q1] …The NP-hardness proof (Theorem 3.1) assumes ReLU activations and specific perturbation bounds. How generalizable are these results to other activation functions (e.g., sigmoid) or arbitrary perturbation norms?
[A1] We will clarify that our results hold for $l_p$ norms with $p = 1, 2, \cdots... | Summary: This paper considers the problem of defining and correlating diversity with algorithmic performance in adversarial ensemble learning. The authors first prove that precisely calculating the diversity in adversarial ensemble learning is an NP-Hard problem. They then propose a new diversity decomposition for adve... | Rebuttal 1:
Rebuttal: [Q1] … a gap between NP-hard problem to first-order approximation …. only consider first order approximation since the complete analysis is a NP-hard problem.
[A1] We will clarify that the first-order approximation is motivated from previous first-order approximation methods for robust learning [... | null | null | null | null | null | null |
Policy Filtration for RLHF to Mitigate Noise in Reward Models | Accept (poster) | Summary: This paper finds that the reliability of the reward model varies across responses assigned with different rewards. Motivated by this fact, this paper considers filtering the samples whose rewards may be unreliable to improve the signal-to-noise ratio during policy learning, resulting in Policy Filtration for P... | Rebuttal 1:
Rebuttal: # Author Response for Reviewer kkSM
We thank the reviewer for highlighting these points.
## Novelty of this paper
While simple, PF-PPO’s contribution lies in its *universal effectiveness* for RLHF noise mitigation. Key innovations:
1. **Empirical Validation:** Extensive experiments across doma... | Summary: This paper introduces Policy Filtration for Proximal Policy Optimization (PF-PPO), a reinforcement learning from human feedback (RLHF) method that addresses reward model noise by selectively training on samples where rewards are most reliable. Observing that reward models are more accurate for extreme (high/lo... | Rebuttal 1:
Rebuttal: # Author Response for Reviewer 2Fwc
We appreciate the reviewer’s questions.
## Why does BW perform the best?
BW selects extreme samples with high/low rewards, which are most reliably aligned with actual scores (Fig. 1). Mid-reward samples often mix correct/incorrect elements (e.g., non-standard... | Summary: This paper introduces a novel method, Policy Filtration for Proximal Policy Optimization (PF-PPO), to address the challenge of reward model inaccuracy in Reinforcement Learning from Human Feedback (RLHF). The authors propose a filtering mechanism to select samples with more reliable rewards, thereby improving ... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer DFFh
We thank the reviewer for the positive and constructive feedback.
## W1: Theoretical Motivation
While PF-PPO is empirically driven, we ground our approach in the signal-to-noise ratio principle: ambiguous samples (mid-reward) introduce conflicting gradients ... | Summary: The authors introduce Policy Filtration for Proximal Policy Optimization (PF-PPO). Their key insight is that the reward signal is more useful in cases of high-reward or low-reward and design an algorithm around exploiting this by filtering the samples used in PPO based on their quality as measured by some heur... | Rebuttal 1:
Rebuttal: # Author Response to Reviewer Rkmi
We thank the reviewer for the careful investigation and fruitful discussion.
## Generalization to Messy Reward Scenarios
While our experiments focus on code and math tasks with verifiable rewards, we acknowledge the importance of evaluating PF-PPO in scenari... | null | null | null | null | null | null |
On the Importance of Embedding Norms in Self-Supervised Learning | Accept (poster) | Summary: This paper explores the role of embedding norms in self-supervised learning (SSL). It shows that embedding norms are crucial for SSL models in two key aspects: they influence convergence rates and encode model confidence. The study demonstrates that smaller embedding norms are associated with unexpected sample... | Rebuttal 1:
Rebuttal: Thank you for the helpful suggestions and the in-depth review! We include experiments and discussion in response to your questions on the SSL methods used in the paper, the training epochs and the applicability/novelty of our analysis. Responses to individual questions are below:
> "while the pap... | Summary: The paper proposes that the norm of the embeddings play an important role that may affect both optimization (convergence) and generalization properties of self-supervised learning methods. The paper makes an analytical observation about how embedding norm can slow down convergence. The paper argues about a mod... | Rebuttal 1:
Rebuttal: Thank you for the extensive analysis and suggestions for how to improve our work! Among other things, our rebuttal includes experiments on the Tiny-ImageNet dataset, on additional models, additional probes and on how the embedding's rank corresponds to the embedding norm. We respond to individual ... | Summary: The article examines the structure of the gradient expression for the InfoNCE SSL objective. Building on a previous result, this gradient expression is reformulated to emphasize that:
- The gradient involves a projection onto a subspace orthogonal to the embedding vector with respect to which the gradient is ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. Regarding Theorem 3.4's applicability, we appreciate your insights about potential mitigations - weight decay, scaling gradients by embedding norms, and adaptive optimizers. We note that our paper systematically analyzes how the first two affect SSL training in ... | Summary: This paper investigated the relatively overlooked area of embedding normalization in self-supervised learning (SSL), as most prior works default to using cosine similarity between embedding vectors -- which normalizes by the product of magnitude of both vectors -- and effectively projects data onto a unit hype... | Rebuttal 1:
Rebuttal: Thank you for the kind words regarding our paper. We respond to your questions below.
> "The weakness would be the proposed methods, especially the cut-initialization, are relatively brutal and less innovative"
Regarding the cut-initialization, we agree with this point and it is something we del... | Summary: This submission explores the embedding norms interaction with SSL training dynamics, where cosine similarity is commonly used to map the data to a hypersphere. This paper studies the gradients of the cosine similarity loss (and the InfoNCE loss) revealing that while gradients are inversely scaled by the norm o... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and in-depth review! Below, we address your concerns by adding experiments on Tiny-ImageNet and testing additional models (BYOL, MoCo v3, Dino), which confirm the generality of our theoretical and experimental results. Furthermore, we clarify the novelty of our analys... | null | null | null | null |
Should Decision-Makers Reveal Classifiers in Online Strategic Classification? | Accept (poster) | Summary: This paper mainly proposes regret bounds for online strategic classification under two novel settings: (i) Revealed-Adv where the agents can still manipulate their feature arbitrarily even when the feature vector after manipulation is still negative. In this case the decision-maker makes $\Omega(k_{in})$ times... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We address them below.
> adversarial tie-breaking
We appreciate the reviewer’s concern and want to clarify that we do *not* intend to make adversarial tie-breaking a realistic behavioral assumption. Instead, we use it as a conceptual and technical intermed... | Summary: This work considers a strategic online classification setting. At each round $t$, the learner selects classifier $h_t : \mathcal{X} \in \{0,1\}$ and an agent with true feature $x_t \in \mathcal{X}$ and label $y_t \in \{0,1\}$ selects a manipulated feature $v_t \in \mathcal{X}$. After the round, $v_t$ and $y_t$... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the thoughtful comments. We address them below.
> lower bound statement in Theorem 4.4
Thanks for catching the issue in the theorem statement. The lower bound in Theorem 4.4 consists of two parts: $\min( d/\log(1/\gamma), |H|)$ and $d \cdot k_{\text{in}} \c... | Summary: This paper studies how hiding the classifier from the agents affects the performance of strategic classification. The result shows that hiding the classifier from the agents significantly increase the number of mistakes the decision maker would make, in proportion to the maximum in-degree of the manipulation g... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and for examining our proofs. We address them below.
> infinite/continuous feature space
First, we would like to clarify that our model does not require the feature or action space to be finite: one can define a generic manipulation graph structure over any ... | Summary: This work studies an online classification setting, where arriving points (agents) can manipulate their features according to a manipulation graph, where the rationale to to obtain more favorable predictions. Under a realizability assumption, the authors study a scenario where the decision maker does not revea... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments. We address them below.
> revealing the past deployed policies
We agree that the decision-maker might not want to reveal their past models explicitly. However, historical outcomes often reveal past classifiers implicitly. For example, we can observe which st... | null | null | null | null | null | null |
Provably Improving Generalization of Few-shot models with Synthetic Data | Accept (poster) | Summary: The paper presents a theoretical framework and corresponding algorithm to enhance the generalization of few-shot learning models by leveraging synthetic data. Guided by our theoretical generalization error bounds, the authors introduce a novel loss function and training paradigm designed to jointly optimize d... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for your positive evaluation and valuable feedback to our work. We would like to address your remaining concerns as follows:
*1. Results for more extreme few-shot conditions*
Thanks for your insightful suggestion. We conduct experiments on 3 datasets that was ... | Summary: The paper tackles the problem of few-shot image classification, where limited labeled data restricts model generalization, by proposing a theoretically grounded approach that augments real data with synthetic data, such as that produced by Stable Diffusion. It introduces a novel test error bound for models tra... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for praising our work and your positive evaluation. We would like to reiterate the key novelties of our work:
1. We introduced a novel generalization bound for a model trained with synthetic data augmentation. It suggests an effective way to generate synthetic s... | Summary: The paper addresses the challenge of improving few-shot image classification by augmenting real data with synthetic data. It identifies the distribution gap between real and synthetic data as a key obstacle. The paper presents a theoretical framework to quantify the impact of this distribution discrepancy on s... | Rebuttal 1:
Rebuttal: We thank the reviewer for your positive evaluation and valuable suggestions. We address your concerns below.
*1. Key assumptions made in deriving the generalization bounds, and their realisticity*
The only assumptions are (1) data samples are i.i.d. and (2) loss function is bounded and Lipschitz... | null | null | null | null | null | null | null | null |
Flexible and Efficient Grammar-Constrained Decoding | Accept (poster) | Summary: - The paper presents a new algorithm for grammar-constrained decoding (GCD) that significantly improves the efficiency of both offline preprocessing and online token masking. The key innovation is a combined analysis of the LLM token vocabulary and set of CFG terminals, which precomputes a lexer-state-dependen... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. Below, we provide a detailed response to each comment and question.
> The paper does not present detailed proofs for their theoretical claims
> a more in-depth analysis of the properties of the realizable terminal sequences and their impact on ... | Summary: The authors provide a more computationally efficient method, called GREATGRAMMA, for computing token masks for CFG-constrained decoding from LLMs. In particular, they address the misalignment between CFG terminals and the LLM token vocabulary, and tricks for speeding up online mask computation during decoding ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. In the revised paper, we will correct all typos, clarify the notation pointed out by the reviewer, and provide rigorous definitions and proofs in the appendix. Below, we provide a detailed response to each comment and question.
> the method is ... | Summary: Given a language model and a certain grammar, grammar-constrained decoding (GCD) aims to ensure the output of the language model adheres to the grammar. However, this is challenging because, typically, the token vocabulary for a language model does not align with that of the grammar. In this paper, the authors... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback. Below, we provide a detailed response to each comment and question.
> is this method masking out other tokens that might also be allowed?
The masking algorithm of GreatGramma is sound and complete under the assumptions on the lexer stated in t... | Summary: This paper describes a flexible & efficient implementation of grammar-constrained decoding for LLMs. Grammar-constrained decoding uses a (usually deterministic) CFG to constrain the output space of LLM decoding. A main challenge in apply such a constraint is the mismatch between the alphabet of the constraint ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, which will significantly improve the paper. We believe ICML is an appropriate venue as it has previously published similar work in constrained decoding (e.g., https://openreview.net/forum?id=pXaEYzrFae last year).
Below, we provide a detailed response to ... | null | null | null | null | null | null |
Decoding Rewards in Competitive Games: Inverse Game Theory with Entropy Regularization | Accept (poster) | Summary: This paper studies inverse game theory for two-player zero-sum Markov games under entropy regularization (quantal response equilibrium): instead of best responding, each player plays a mixed strategy with softmax probability $\frac{e^{\eta u(a)}}{\sum_{a\in\mathcal A} e^{\eta u(a)}}$. Given observed actions s... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and for carefully checking the proofs of our theorems.
## Concerns about the Asymptotic Notation
We appreciate your observation regarding the use of asymptotic notation in Theorems 2.3, 2.4, and 2.5. To streamline presentation, we omitted terms ... | Summary: Under the assumption that agents are playing a QRE (a relaxation of Nash equilibrium that includes an entropy regularization term), this paper presents a method for learning the rewards of each agent based on a dataset of interactions in a finite-horizon zero-sum Markov game (or the special case of a single no... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for thoroughly evaluating our work and for your valuable comments. We hope our reply will address your concerns and questions.
### (Q1) Substantive Implications of the Non-singularity Assumption
The non-singularity assumption on the feature covariance matrix $\Psi_h$ ens... | Summary: The paper analyzes the problem of identifying the utility in a zero-sum game from observations of a quantal response equilibrium policy. The authors provide an algorithm and theoretical guarantees on the recovered utilities. Moreover, they extend their analysis to zero-sum Markov games under a linear MDP assum... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful and constructive feedback! We greatly appreciate your positive assessment of the theoretical soundness and methodological consistency of our work. Below, we address your specific concerns and suggestions.
### Relation to Single-Agent IRL Identificatio... | Summary: The submission considers inverse reinforcement learning in games. Specifically, the authors first study the conditions for the problem to identifiable, and the propose a methodology to estimate the reward function. Both theoretical analysis and empirical results are provided to justify the proposed method.
Cl... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your valuable feedback and for recognizing the relevance and significance of our contributions. Below, we address your concerns regarding the necessity of the rank condition (Proposition 2.2), comparisons with related works, and competing methods in experiments.
####... | null | null | null | null | null | null |
The Butterfly Effect: Neural Network Training Trajectories Are Highly Sensitive to Initial Conditions | Accept (poster) | Summary: The submission investigates the sensitivity of neural network training outcome to initial conditions. Building upon related work in optimization and training dynamics, it investigates the conditions for stability and identifies a chaotic early stage in which small perturbations cause trajectory divergence. Exp... | Rebuttal 1:
Rebuttal: Thank you for your substantive review, to which we have replied below.
## F. Comparing Dissimilar Tasks and Scales
> Experiments are performed mostly on relatively small vision models with relatively simple tasks. [...] To compare different domains, one would have to compare models in the same r... | Summary: In this work, the authors study how the sensitivity of training trajectories in neural network training depends changes with the distance to the initialization. The authors characterize the sensitivity through the divergene of training trajectories, which is measured in $L_2$ distance, loss barrieres, defined ... | Rebuttal 1:
Rebuttal: Thank you for the detailed review, which we address by topic below.
## E. $L^2$ Divergence
> Also the $L^2$ distance is an intuitive metric to quantify the divergence between two runs. [...] Can the authors therefore provide similar Figures as the ones in Figure 2 or Figure 3, where the error ba... | Summary: This paper studies the impact of perturbation on SGD in an empirical way. The authors focus on the condition under which the perturbation during training can lead to convergence to another basin. The authors prepared perturbation in different directions of various scales that is applied to training at differen... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We have addressed the quoted comments and questions point-by-point below.
## C. Non-Linear Connectivity
> Due to the continuous symmetries, there are equivalent local minima that should be considered the same basin and are not linearly connected. [...] Howev... | Summary: The paper studies the impact of applying isolated perturbations to model parameters during different training and fine-tuning phases. The analysis provides insights into model training stability using three quantities measuring parameter similarity or functional similarity. The findings suggest that models bec... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. Please find our replies below for the quoted points.
## A. Other Similarity Measures
> I'm wondering how other similarity measures differ from the perspective presented by the authors, e.g., CCA or CKA index, which could be an interesting add-on to the curre... | null | null | null | null | null | null |
Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective | Accept (poster) | Summary: This paper focuses on the theoretical analysis of representation forgetting in Continual Learning scenarios. While representation forgetting has been introduced previously, the focus was largely experimental and the main contribution of the proposed work is to focus on the theoretical aspect of representation ... | Rebuttal 1:
Rebuttal: We thank the reviewer cno5 for the detailed review and constructive suggestions. We appreciate your acknowledgements that the theoretical aspects of this paper are **clear and adequately justified**, and that the findings are **interesting and novel**. Below we show our reply on your comments and ... | Summary: In this works, the authors introduce a novel measure of forgetting in the hidden layers of deep neural networks in continual learning setting. They derive an upper-bound for the proposed representation discrepancy measure and the convergence rate of this measure under a set of assumptions. Additionally, the au... | Rebuttal 1:
Rebuttal: We thank the reviewer 4iQx for the detailed review and constructive suggestions. We appreciate your acknowledgements that the problem addressed in this manuscript is potentially **important and interesting**, and that our results are **clearly distinct from the previous works**. Below we show our ... | Summary: This paper introduces a novel metric—termed representation discrepancy—to quantify the degradation of internal feature representations (i.e., representation forgetting) in continual learning. By framing the forgetting problem in terms of a minimum alignment error between hidden layer representation spaces via ... | Rebuttal 1:
Rebuttal: We thank the reviewer 9B8C for the detailed review and constructive suggestions. We appreciate your acknowledgements that our theoretical and experimental results are generally **well-presented and convincing**, and that our **proofs are methodologically sound**. Below we show our reply on your co... | Summary: This paper introduces a novel metric for measuring representation forgetting in continual learning and derives an upper bound for this metric. The theoretical findings provide valuable insights, which are further validated through experiments on real image datasets.
Claims And Evidence: Yes.
Methods And Eval... | Rebuttal 1:
Rebuttal: We thank the reviewer 1dag for the detailed review and constructive suggestions. We appreciate your acknowledgements that our **proposed metric is a reasonable choice** for facilitating theoretical analysis and that this paper is **well-written**. Below we show our reply on your comments and quest... | null | null | null | null | null | null |
A Recipe for Causal Graph Regression: Confounding Effects Revisited | Accept (poster) | Summary: This paper addresses the challenge of adapting causal graph learning (CGL) techniques from classification to regression tasks, introducing a framework called causal graph regression (CGR). The authors identify two key innovations: (1) an enhanced graph information bottleneck loss function that, unlike previous... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer iAdk for the useful feedback. Detailed responses are provided below. We kindly ask the reviewer to reconsider their score if the following clarifications resolve the concerns.
### Q1. Why are gains on GOOD-ZINC larger than on ReactionOOD?
GOOD-ZINC is large-scale and h... | Summary: This paper investigates causal graph regression (CGR), extending causal graph learning (CGL) techniques, which have been successful in classification tasks, to the more challenging regression setting. The authors introduce a novel approach that adapts causal intervention techniques to regression through the us... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer jru1 for the thoughtful and encouraging feedback. We appreciate your recognition of our method’s motivation, design, and empirical support. Below we address each of your comments in detail.
### Q1. Explain the fundamental difference between graph classification and re... | Summary: This paper proposes an improved causal graph regression method by an enhanced graph information bottleneck loss function and a contrastive learning loss from generated counterfactual graphs. Experiments on OOD datasets confirm its generality.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
The... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer HaWt for the thorough and constructive feedback. We have carefully addressed the raised concerns and suggestions. We kindly ask the reviewer to reconsider their score if the following points resolve the reservations with our work, or to provide more pointers for us to a... | null | null | null | null | null | null | null | null |
LoRA-One: One-Step Full Gradient Could Suffice for Fine-Tuning Large Language Models, Provably and Efficiently | Accept (oral) | Summary: This paper presents a theoretical analysis of Low-Rank Adaptation (LoRA) for efficient fine-tuning of large language models. The main contributions are:
- Theoretical analysis showing that LoRA's gradient updates align with the singular subspace of the full fine-tuning gradient.
- Introduction of a spectral in... | Rebuttal 1:
Rebuttal: We greatly appreciate reviewer's efforts and constructive feedback.
---
>**Q1** More diverse experimental tasks
We extend our method to fine-tune T5 model on a subset of SuperGLUE [1] datasets, which is more challenging than GLUE and widely used in fine-tuning papers [2-5]. We use full fine-tun... | Summary: This paper investigates methods to enhance the performance of Low-Rank Adaptation (LoRA). The authors make two key discoveries: (i) LoRA tends to align with a specific singular subspace in a single step, and (ii) the use of preconditioners significantly improves convergence in high-rank scenarios. Building on ... | Rebuttal 1:
Rebuttal: We greatly thank the reviewer's efforts and constructive comments.
>**Q1** *Appropriateness of title*
**A1** The title is derived from our theory: under proper initialization from one-step full gradient, we can recover $\Delta$ to large extent **at initialization**, see Prop. 3.3 (linear) and Le... | Summary: The paper provides a theoretical analysis of LoRA fine-tuning by showing that a single full gradient step naturally aligns the LoRA updates with the top singular subspace of the full gradient, and by introducing a spectral initialization strategy, it can effectively recover the downstream low-rank target befor... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s efforts and the positive support.
---
>**Q1** *The theory is detailed and rigorous, although it is unclear how generalizable these insights are to the practical setup of LoRA fine-tuning deep transformers.*
**A1** We are greatly thankful for the reviewer to p... | Summary: This paper focuses on the learning dynamics of Low-Rank Adaptation (LoRA) and proposes improvements in initialization and gradient preconditioning.
The authors analyze both linear and nonlinear matrix factorization cases, where the objective is to minimize $\|\tilde{X}(W+AB)-\tilde{Y}\|_F^2$ using gradient de... | Rebuttal 1:
Rebuttal: We thank reviewer's effort and constructive comments on this work. We fixed the typo on "algin" and address your concerns as below.
>**Q1** Simplistic model
**A1** Our formulation **can be not regarded as the matrix factorization (MF) problem**, which is strictly defined as $\min_{A, B} ||AB-\De... | null | null | null | null | null | null |
Online Conformal Prediction via Online Optimization | Accept (poster) | Summary: The authors propose an algorithm for online conformal prediction that achieve both long-run deterministic coverage as well as conditional coverage under stochastic data which is well-behaved. The idea is to train a predictive model of the quantile on the sequence of data. Different results are assumed under di... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work.
**However, I couldn’t find any long-run coverage results in the tables in the main text.**
The long-run coverage for all the methods in Table 1 is at least 0.87 - we will make this clearer in the final version. We have also attached plots... | Summary: The paper proposes an online conformal prediction method that integrates stochastic optimization to improve coverage guarantees. It claims existing methods use bang-bang control and lack time-conditional guarantees. The proposed method supposedly achieves better theoretical guarantees and outperforms baselines... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments. We first provide a few general remarks.
Our work provides stronger guarantees than standard online conformal works, by offering not just the standard adversarial marginal guarantee, but also a stronger conditional one when the data is non-advers... | Summary: This paper introduces a conformal inference algorithm in the online setting that achieves both long-term coverage guarantees in adversarial settings, as well as expected coverage (conditioned on the past sequence) that converges to the desired coverage rate in certain stochastic settings. Many existing online ... | Rebuttal 1:
Rebuttal: **I couldn’t find the long-term coverage rate reported for any of the experiments...I would have liked to see the trade-off it has when it comes to long-term coverage.**
At this link [https://drive.google.com/file/d/1AT1hbP3ohYowIt0_Hy209yadYvZjl9cJ/view?usp=sharing], we have attached additional ... | Summary: This paper is looking at the problem of online conformal prediction, focusing on deriving stronger (conditional) guarantees than long run marginal coverage. They propose to look at asymptotic absolute value coverage deviation from the nominal value at each time step, conditioned on all the past observations. T... | Rebuttal 1:
Rebuttal: **A very similar notion of conditional coverage has been previously discussed by [1], in a stochastic iid batch setting, named mean squared conditional error**
We thank the reviewer for pointing us to this reference. We would like to verify that it refers to [1], and we would be happy to incorpor... | null | null | null | null | null | null |
A Hitchhiker's Guide to Scaling Law Estimation | Accept (poster) | Summary: This work investigates the challenge of fitting scaling laws in the language model domain, in particular:
- How accurate we should expect scaling laws to be? (The authors show ~ 4% Absolute Relative Error (ARE) at best).
- How does the shape of scaling laws vary with architecture, in order for the community to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their interest and for deeming our work “extremely valuable” as well as for the sincere effort in ensuring all the details are in place. We believe the two most pressing issues found are, in fact, simpler to explain than one might expect and hope the reviewer will agree w... | Summary: The authors address challenges in scaling law estimation by compiling a dataset of training losses and evaluations from 485 pretrained language models. Through extensive empirical analyses, they establish concrete best-practice guidelines for efficiently predicting the performance of new, larger models without... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and feedback and for proposing that the paper should be accepted. In essence, we also agree that this paper is a meta-analysis trying to figure out what current trends tell us about the world rather than prove that this has to be the case forever. We do belie... | Summary: This paper provides a comprehensive analysis of scaling laws in large language model (LLM) training, focusing on how to estimate and interpret scaling laws effectively. The authors construct and release a large-scale dataset containing training losses and evaluations from several pre-trained models, enabling t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback and the accurate points made are encouraging that some deep points about scaling laws indeed pass to readers.
Regarding the two questions:
1. How well do these findings generalize to architectures beyond transformers?
Our experiments focused on two... | Summary: The paper is a meta-analysis of scaling law fitting, focusing on a popular power law relating the pretraining loss to the model size and number of training tokens. The paper offers guidelines for how accurate these scaling laws can and should be and how to best estimate them given available resources.
## Upda... | Rebuttal 1:
Rebuttal: We thank the reviewer for the deep read, care for details and supportive stance.
The main concern raised by the review had to do with the estimation of scaling law parameters (and why some were reported as negative). As discussed below, this was (fortunately) a mistake in our presentation rather... | null | null | null | null | null | null |
Self-Supervised Learning of Intertwined Content and Positional Features for Object Detection | Accept (poster) | Summary: Post-rebuttal
After reading the comments by reviewers v6Kv and mPP8, I agree that contrastive learning for dense prediction can offer advantages over autoencoder-based methods, such as more efficient training without heavy fine-tuning. I also agree that the proposed method introduces some technical novelty be... | Rebuttal 1:
Rebuttal: **Q1. Why use contrastive learning for dense representation? ...**
The importance of this study can be explained from practical and exploratory perspectives.
Practical Perspective: Although the CVPR2022 MAE results demonstrate high object detection performance, they incur extremely high computa... | Summary: The paper proposes a novel self-supervised learning (SSL) framework tailored specifically for object detection (OD) and instance segmentation (IS). The key idea is to integrate positional information more effectively by introducing a learnable positional encoding field that is aligned with the image cropping p... | Rebuttal 1:
Rebuttal: **Q1. Have you experimented with stronger backbones such as ViT-Large/16?...**
Due to insufficient computational resources, we have not been able to conduct such large-scale experiments comprehensively; however, preliminary results confirm the scalability of our approach. Nonetheless, we believe ... | Summary: The paper proposes and investigates a novel positional encoding method and an extension to DINOv2 loss that incorporates positional masking for better SSL pretraining of Object Detection and Instance Segmentation Vision Transformer backbones. The method achieved competitive performance on COCO and ADE20K. Abla... | Rebuttal 1:
Rebuttal: **W1. One weakness of this work is the unclear cause of the stronger vertical artifact, compared to horizontal, in the attention map when box-based masks are used.**
Two potential explanations may account for the artifact being more pronounced vertically than horizontally: the statistical bias in... | Summary: This work extends the teacher-student SSL approach of DINO v2 with an additional task with the goal to improve the dense prediction capability of the trained model. Specifically, during training the student network is either tasked with the alignment of masked positional encoding as well as the standard alignm... | Rebuttal 1:
Rebuttal: **Q1. The method improves object detection on COCO, but the improvements in segmentation are limited. The fact that the position sampling on its own, even without the additional training task shows significant gains is interesting. The importance of the sampling strategy (constant, uniform, beta) ... | null | null | null | null | null | null |
ASRC-SNN: Adaptive Skip Recurrent Connection Spiking Neural Network | Reject | Summary: This research considers neurons and recurrent structures as an integrated system and systematically analyzes gradient propagation along the temporal dimension, uncovering a difficult gradient vanishing problem. To tackle this challenge, the study proposes innovative architectural modifications that enhance the... | Rebuttal 1:
Rebuttal: We thank the reviewer's feedback. We also hope the reviewer will take note of other strengths of the paper, such as the positive points mentioned by Reviewer 3VEv、 Reviewer keA4 and Reviewer 9KzL. Below, we provide point-by-point responses to the weeknesses.
>Weakness 1: The ASRC model does not o... | Summary: This paper proposes an Adaptive Skip Recurrent Connection (ASRC) framework for Spiking Neural Networks (SNNs) to address gradient vanishing in long-term temporal modeling. By unifying the analysis of neurons and recurrent structures, the authors identify gradient propagation challenges and introduce SRC (fixed... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer 9KzL for his review. Below we provide point-by-point responses to the Weaknesses.
>Weakness 1: The length of the temporal can be marked below the dataset to make it more intuitive.
Response 1: We thank the reviewer for the helpful suggestion and will implement them... | Summary: The paper introduces ASRC-SNN, a spiking neural network architecture that incorporates adaptive skip recurrent connections to improve long-term temporal modeling. It identifies and addresses the gradient vanishing problem in recurrent spiking neural networks (RSNNs), which occurs when gradients propagate over ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thorough and insightful comments. Below we provide point-by-point responses to the Questions.
>Questions 1: What is the computational overhead of learning adaptive skip spans compared to using fixed skips or standard recurrent connections? A runtime comparis... | Summary: This paper proposes the Skip Recurrent Connection (SRC) as a replacement for the vanilla recurrent structure and also proposes the Adaptive Skip Recurrent Connection (ASRC), a method that can learn the skip span of skip recurrent connection in each layer of the network.
Claims And Evidence: 1. This paper has ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's comments, especially some valuable questions raised. We also hope the reviewer will take note of other strengths of the paper, such as the positive points mentioned by Reviewer keA4 and Reviewer 9KzL. Below, we provide point-by-point responses to the question... | null | null | null | null | null | null |
Optimal Transfer Learning for Missing Not-at-Random Matrix Completion | Accept (poster) | Summary: This paper studies the problem of matrix completion with missing not-at-random mechanisms, where the observation pattern is row/columns-wise. Under such missing/observation family, the authors establish the minimax lower bound for entrywise estimation error. With side information, the authors propose a computa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and for highlighting that our claims are supported by rigorous theoretical investigations and numerical experiments. We will fix typos in the revision, and we address all other comments below.
## Our methods perform best under new evaluation met... | Summary: This paper studies transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting, which is motivated by biological problems. The problem is challenging because entire rows and columns of the target matrix are are missing, making direct estimation impossible. This paper introduces a source m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and for highlighting the relevance of our problem (MNAR matrix completion with transfer learning) to biological applications. We address their comments below.
## Our minimax results guide algorithm design.
The practical significance of our min... | Summary: This paper studies a problem in which there are two matrices $P$ and $Q$ of the same dimension, where a noisy version of $P$ is observed and a noisy and partial view of $Q$ is observed. $P$ is known to be low-rank, and the two matrices are related via distribution shift. The objective is to recover the matrix ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and for highlighting that our paper has strong empirical results, and is comprehensive in its analysis of an interesting problem. We address their comments below.
## We will reorganize the writing to first introduce the estimation framework, and... | Summary: The authors study matrix completion in the MNAR setting under transfer learning. They establish minimax bounds for entry-wise estimation of target values under both active and passive sampling settings. Additionally, they propose a computationally efficient minimax-optimal estimator—leveraging the tensorizatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and for highlighting that our paper is well written and presents a sound theoretical framework. We address their comments below.
## The manuscript contains results for a non-transfer MNAR baseline (BC22).
The method of BC22 (IEEE Transactions o... | null | null | null | null | null | null |
Subgoal-Guided Policy Heuristic Search with Learned Subgoals | Accept (poster) | Summary: The paper proposes a new approach to training policies for search. The authors leverage subgoal guidance and this way they show how to get the training signal even from unsolved episodes. By experiments in 4 environments, they show that the proposed approach improves sample efficiency of the training procedure... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback.
**Concern 1**
Each node generation for LevinTS/PHS requires a single policy call (plus heuristic for PHS). Each node generation for our approach requires K VQ-VAE calls to generate the K subgoal observations, K policy calls for the low-level poli... | Summary: The paper proposes a new approach for utilizing policy tree search by the inclusion of learned sub-goals. The subgoals are learned online as the tree search expands during a Bootstrap process while attempting to solve problems. One key innovation is the utilization of failed solution trees as data as well (sim... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback.
**Weakness 1**
We understand the reviewer's concern with running time as some of the gains we have in terms of expansions disappear when solving easier problems. To address this, we ran experiments on a much more difficult version of BoulderDas... | Summary: The authors propose a new way to generate policies for deterministic tree search algorithms. Their method to do so is by learning to generate sub-goals using a VQVAE where the subgoals are recognized , learning to reach these subgoals with a low level policy and a high level policy over subgoals.
Claims And E... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments and feedback.
**Question 1.**
The environment domains chosen are common amongst related works. In terms of complexity, Sokoban is PSPACE-complete [1], CraftWorld and Box-World are NP-hard [2]. BoulderDash requires collecting multiple diamonds before reaching the ... | Summary: - The paper proposes an algorithmic framework for best-first search intended to solve deterministic search problems. The primary contributions of the paper are algorithmic and empirical. The main algorithmic idea is to learn control knowledge to speedup search. Subgoals here refer to state abstractions represe... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback.
**Question 1**
Thank you for your suggestion to run on more difficult instances. We ran additional experiments on a much larger version of BoulderDash, following the same procedure from **Section 4.3**. We also tried increasing the network size u... | null | null | null | null | null | null |
Stochastic Deep Restoration Priors for Imaging Inverse Problems | Accept (poster) | Summary: This paper introduces stochastic deep restoration priors, a framework leveraging an ensemble of pre-trained restoration models as priors for solving imaging inverse problems. The authors claim they minimizes a regularizer based on degraded observation likelihoods, generalizing denoiser-based methods like RED a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback.
>1. *For concerns in Methods And Evaluation Criteria:*
Prompted by your comment, we ran additional experiments that we will include in the supplementary material of the paper. The new setting considers non-Cartesian sampling for CS-MRI, using a ... | Summary: This paper introduces ShaRP, a stochastic regularization for linear inverse problems of the form y=Ax+e. This regulatization relies on MMSE (approximated) restoration machines R(*) for problems of the form s=Hx+n, where H is randomly chosen. By injecting the gradient of this regularization within the iterative... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback.
> 1. *It is unclear what ShaRP is after - is it a better MAP estimate? or perhaps an MMSE one? Clearly, this is not a posterior sampler. This question is critical, ..., as they they tend to be somewhat blurry.*
This is a great point. ShaRP is no... | Summary: The paper presents Stochastic Deep Restoration Priors (ShaRP), a framework for imaging inverse problems that leverages an ensemble of pre-trained deep restoration models. ShaRP uses a stochastic gradient descent approach where, in each iteration, a random degradation operator (with added Gaussian noise) is app... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback.
> Response to concerns in Claims and Evidence
1. Generalization over configurations: We wanted to highlight that the supplementary material contains evidence supporting the robustness of ShaRP across diverse inverse problems without retraining, p... | Summary: The paper develops an plug-and-play imaging restoration algorithm that can use MMSE estimators trained to solve an arbitrary inverse problem involving linear forward operators and white gaussian noise (not just denoising), to solve a target linear inverse problem. That is, one can, for example, use a network t... | Rebuttal 1:
Rebuttal: We thank the reviewer for feedback and thoughtful comments on our work.
> 1. *The paper differentiates itself from related self-supervised methods by stating "Ambient DMs seek to sample from px using DMs trained directly on undersampled measurements. Thus, during inference Ambient DMs assume acce... | null | null | null | null | null | null |
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