title stringlengths 15 163 | paper_decision stringclasses 4
values | review_1 stringlengths 853 32.6k | rebuttals_1 stringlengths 0 15.1k | review_2 stringlengths 1.03k 35.6k | rebuttals_2 stringlengths 0 15.1k | review_3 stringlengths 807 27.4k ⌀ | rebuttals_3 stringlengths 0 15k ⌀ | review_4 stringlengths 780 22.2k ⌀ | rebuttals_4 stringlengths 0 15.1k ⌀ | review_5 stringclasses 171
values | rebuttals_5 stringclasses 166
values | review_6 stringclasses 25
values | rebuttals_6 stringclasses 24
values | review_7 stringclasses 4
values | rebuttals_7 stringclasses 4
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Control and Realism: Best of Both Worlds in Layout-to-Image without Training | Accept (poster) | Summary: This work introduces WinWinLay, a novel training-free optimization strategy for layout-to-image generation using text-to-image diffusion models. The paper tackles two main drawback of previous approaches for layout-to-image generation: (1) that the generated objects are often not precisely places within the gi... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer Reviewer YGgi for the thorough and constructive feedback on our manuscript. We are more than happy that the reviewer finds our problem definition is reasonabl, theoretical analysis illustrative, and superior performance. We would like to address the concer... | Summary: This work aims to achieve high-quality generation for the Layout-to-Image generation task without requiring any training data. It begins by providing a theoretical analysis of existing backward guidance methods and introduces a novel Non-local Attention Energy Function, which enables the model to better respec... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer QbsF for the thorough and constructive feedback on our manuscript. We are more than happy that the reviewer finds our paper is clearly structured, comprehensive and insightful analysis, and superior performance. We would like to address the concerns as bel... | Summary: Layout-to-image generation faces two significant challenges: a.) imprecise object localization and b.) the presence of unrealistic artefacts in the final output. To address these issues, the authors propose Win-Winlay, a novel method that incorporates:
1. **Non-Local Attention Energy Function**: This function ... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer hxyx for the thorough and constructive feedback on our manuscript. We are more than happy that the reviewer finds our paper is well-structured, theoretical analysis illustrative, and exhaustive ablation studies. We would like to address the concerns as bel... | Summary: This paper is a new method in the layout to image domain.
Two key problems this paper tries to address:
* Imprecise location
* Unrealistic artifacts
The core contribution of this paper:
* A non-local attention energy functino
* An adaptive updating mechanism to balance the spatial control and image qua... | Rebuttal 1:
Rebuttal: We would like to sincerely thank Reviewer z1Kv for the thorough and constructive feedback on our manuscript. We are more than happy that the reviewer finds our writing well-organized, theoretical analysis illustrative, and quantitative improvement promising. We would like to address the concerns a... | null | null | null | null | null | null |
Contextures: Representations from Contexts | Accept (poster) | Summary: The authors propose a framework for understanding representation learning through the concept of contextures, which are the top singular functions of an operator induced by a context variable. The goal is to characterize learned representations across supervised, self-supervised, and manifold learning paradigm... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We are glad that you find our theoretical analysis and the proposed metric valuable. We would like to answer your questions as follows:
## 1. Efficiency
For extracting the top singular functions, the time complexity of kernel PCA is the same as eigendecomposit... | Summary: The author proposes a framework that encapsulates common representation learning methods as learning the joint distribution of inputs and contexts. It’s possible to decompose this joint distribution with eigen-decomposition. This decomposition shows that optimal representations are learned via learning the sub... | Rebuttal 1:
Rebuttal: Thank you for your review.
## 1. Experiments in Section 4.2 (scaling) are updated
We changed the embedding dimension from $d=16$ to $d=128$ (which is more common in practice) and reran the experiment. The results are plotted in https://i.postimg.cc/FRncq3Zb/alignment.jpg
The main observation is t... | Summary: The manuscript presents a framework called contextures, showing that many representation learning methods aim to capture the top singular functions of an operator defined by the relationship between inputs and a context variable. It shows that such representations are optimal for tasks that align with the cont... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We address your concerns as follows:
## 1. Our results are applicable to larger datasets
**We conduct more experiments on larger datasets such as MNIST and CIFAR-10.**
We initially used datasets with $<$ 30K samples (largest: 28,155; see App. F) since most exp... | Summary: The paper introduces a theoretical framework to characterize representations learned by a neural network is: each data sample is represented by a variable and a context variable (e.g. labels in classification settings, augmentations in self supervised learning, or its K nearest neighbors). The authors proposed... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We're glad that you find our theory generally good, interesting and valuable. We address your concerns below:
## 1. Experiments in Section 4.2 (scaling) updated
**We improved the setting and observed much higher alignments.** Due to the 5000-character limit, p... | null | null | null | null | null | null |
Statistical Test for Feature Selection Pipelines by Selective Inference | Accept (oral) | Summary: This paper presents a statistical test to assess the significance of data analysis pipelines, which transform raw data by integrating various analysis algorithms. The paper specifically focuses on feature selection pipelines for linear models, which are composed by value imputation algorithms, outlier detectio... | Rebuttal 1:
Rebuttal: We thank the reviewer for your feedback.
> The probability of a missing value was set to 0.03, which seems quite low. How (or better does) such value can affect the performance of the statistical test?
This probability was set only for experimental convenience. The validity of the proposed metho... | Summary: The authors propose an extension of selective inference techniques from single procedures (lasso, marginal screening) to pipelines. They develop a statistical test that they claim to have better power for data analysis pipelines with multiple, data-adaptive decision points.
Claims And Evidence: The claims of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for your feedback.
> It is not clear how well this procedure could truly generalize, if at all, to more complicated ML models or pipelines. The strong assumptions that are made about error normality and a fixed design matrix seem to be quite central to the validity of the st... | Summary: This paper presents a statistical testing framework based on selective inference (SI) for assessing the significance of features selected through full feature selection pipelines. These pipelines may include steps such as missing value imputation, outlier detection, and feature selection. The key idea is to co... | Rebuttal 1:
Rebuttal: We thank the reviewer for your feedback.
> Line 110: "AD – anomaly detection?" → consider changing to OD - "outlier detection" for consistency with terminology used elsewhere.
Thank you for pointing this out. We change the term to OD in the revised manuscript.
> Figure 3: It is difficult to dis... | null | null | null | null | null | null | null | null |
Sampling Binary Data by Denoising through Score Functions | Accept (poster) | Summary: This paper proposes a generative model for data on the boolean hypercube. The basic idea is to "noise" the data with random sign flips, with expected number of sign flips controlled by a parameter $\alpha$, and then learn to "reverse" this operation which ultimately boils down to learning a denoiser in this pa... | Rebuttal 1:
Rebuttal: Thank you for your review.
Could the reviewer comment on "the authors proceed to develop a Gibbs sampler for the initial distribution"? The algorithm we have studied is not a Gibbs sampler.
Regarding "In my opinion, the methodology lacks motivation", our motivation is that in many applications, ... | Summary: This paper investigates the problem of sampling from distributions over the binary hypercube using a smoothing-denoising framework. Instead of adding Gaussian noise, the authors introduce a novel approach based on Bernoulli noise. To enhance convergence in the denoising step, they leverage proximal sampling me... | Rebuttal 1:
Rebuttal: Thank you for your review.
Regarding "Other Comments Or Suggestions":
- Thank you, we'll correct the typo.
Regarding "Questions For Authors":
- Thank you for the question. Intuitively, kinetic Langevin algorithms or Hamiltonian Monte Carlo also have an auxiliary variable ("velocity" in their c... | Summary: The authors propose a method to sample binary (vector) data by denoising through score functions. They first propose a noise model for binary data where the noise corresponds to a bit flipping with a given probability. Then, they construct the optimal denoiser based on Hamming loss and they show that it corres... | Rebuttal 1:
Rebuttal: Thank you for your review.
Please note in the original DDPM formulation, there is indeed an SDE behind the scenes. In other words, for understanding the theoretical properties of DDPM one has to return to the SDE formulation. The case of measurement accumulation is discrete by nature. Algorithmi... | Summary: The authors introduce a denoising sampling algorithm for distributions supported on the d-dimensional Boolean hypercube. In this discrete context, Gaussian noise does not exist and therefore one has to argue differently. The idea is to noise the target distribution by flipping each coordinate with a given prob... | Rebuttal 1:
Rebuttal: Thank you for your review.
Regarding "Essential References Not Discussed", the concurrent work by Le Tuyet et al. (the paper became public after the ICML submission deadline) is different from our paper, as it is based on a "continuous-time Markov chain," but both address the problem of sampling ... | null | null | null | null | null | null |
Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective | Accept (poster) | Summary: The paper’s central takeaway is that offline RL under trajectory-wide sparse rewards (for LLMs, outcome supervision) and dense rewards (process supervision) are statistically equivalent (up to polynomial factors in the horizon).
The main contribution is the Change of Trajectory Measure Lemma, a powerful resul... | Rebuttal 1:
Rebuttal: Thank you very much for your support.
***
> Relevant Citations in L416
We will add the citations accordingly.
***
> Assumption on finiteness of $|R|$
Thanks for pointing this out. Even though the current theorems are stated for finite reward models, the change-of-trajectory-measure lemma does... | Summary: This paper shows a theoretical analysis of the statistical equivalence between dense reward trajectories and sparse reward trajectories, which returns the total reward at the end of the episode in offline RL.
**Post-rebuttal**
I read the responses from the authors and other reviewers.
I think this is a theory... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we address the concerns raised in the review.
***
> We don't need to annotate or learn a dense reward model, but sparse reward trajectories could solve all problems. The main weakness is that it is difficult to imagine how this result can be useful.
> Could you p... | Summary: The paper compares process and outcome supervision in reinforcement learning from a theoretical perspective. The central result is showing that RL outcome rewards is no more statistically difficult than RL with step-level rewards, which is shown via a result that states that an offline RL dataset with outcome ... | Rebuttal 1:
Rebuttal: Thank you for the review. Below, we address the concerns raised in the review.
***
> I think some of the claims in the introduction and general framing of the paper are too strong … I think the applicability is probably lower because the reward signals in LLMs are often provided by human oversee... | Summary: This paper challenges the conventional belief that process supervision (step-wise rewards) is statistically superior to outcome supervision (cumulative rewards) in reinforcement learning, particularly for complex tasks like LLM reasoning. The main contributions are:
1. **Theoretical Equivalence**: Under stand... | Rebuttal 1:
Rebuttal: Thank you for your review. Below, we address the concerns raised in the review.
***
> more thoroughly discuss recent works on automated process supervision [1-3]
Thanks for pointing these out, we will add more detailed discussion in the updated version.
***
> Coverage Assumptions
We would li... | null | null | null | null | null | null |
Subspace Optimization for Large Language Models with Convergence Guarantees | Accept (poster) | Summary: The paper critically examines GaLore, highlighting its convergence limitations and proposing GoLore, a robust variant that ensures stochastic convergence. The findings contribute to improving memory-efficient subspace optimization methods for LLM training.
## update after rebuttal
My concerns were addressed i... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our theoretical findings and for the valuable comments. All questions have been clarified as best as we can, and we are glad to address any further comments or questions. We include all new experiments in this **[anonymous link](https://www.hostize.com/v/pcW... | Summary: This paper examines the convergence properties of subspace optimization algorithms for LLM, focusing on GaLore. GaLore is known for memory efficiency in pre-training and fine-tuning LLM, this paper shows that it does not always converge under standard stochastic optimization settings. The authors substantiate ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our theoretical results and appreciate the efforts made to check our convergence proofs. All questions have been clarified as best as we can, and we are glad to address any further comments or questions. We include all new experiments in this **[anonymous li... | Summary: The paper shows that the subspace projection of Galore can be biased when approaching to local minimizer, where the principle component of the projection matrix mainly capture the information of the stochastic noises. Built upon this insight, the paper proposes a method named Golore, which samples the projecti... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our theoretical results and experimental designs, as well as the detailed comments and suggestions. All questions have been clarified as best as we can, and we are glad to address any further comments or questions. We include all new experiments in this **[a... | null | null | null | null | null | null | null | null |
Long-Form Speech Generation with Spoken Language Models | Accept (oral) | Summary: This paper aims to develop a speech language model (LM) capable of modeling long-form speech. The key distinctions of this paper compared to previous studies are as follows: (1) Introducing State Space Models (SSMs) into the speech LM at the speech-token level, resulting in improved perplexity (PPL) and other ... | Rebuttal 1:
Rebuttal: Thank you for your thorough reading of our work!
> W1: SpkrSim and N-MOS reflects speech tokens; decoupled tokenizer/vocoder baselines?
**We agree N-MOS and SpkrSim are primarily due to tokenizer/vocoder**, hence why it felt unnecessary to isolate them from the backbone architecture change as th... | Summary: This paper introduces the first speech-language model ***SpeechSSM***. Two new metrics and a new benchmark are proposed. Experiments and analysis are comprehensive.
Claims And Evidence: **Yes**
Methods And Evaluation Criteria: **Yes**
Theoretical Claims: **Yes**
There is no theoretical claim in this paper.... | Rebuttal 1:
Rebuttal: Thank you for your review and support!
> (Minor) The architecture of SpeechSSM appears somewhat straightforward and could benefit from more innovative design considerations.
As our primary contribution is advancing long-form speech generation over tens of minutes, and being the first work on the... | Summary: The paper proposes SpeechSSM, a spoken language model designed for long-form speech generation. It is based on state-space models enabling efficient generation with constant memory consumption. To evaluate the model on long generations the authors propose LibriSpeech-Long benchmark and new evaluation metrics i... | Rebuttal 1:
Rebuttal: Thank you for your review and thoughtful feedback about our work!
> **Major Weaknesses:**
>
> No human evaluation beyond N-MOS
Note **we have strengthened our MOS results**; see response to Reviewer QbKA (#4).
> LLM-as-judge vs. human correlation analysis
LLM judges align closely with human pr... | Summary: The paper proposes SpeechSSM, a spoken language model based on state-space models that can generate long-form audio in a single pass. The paper also proposes reference-based semantic similarity and LLM-based pairwise judgment to evaluate the generated long-form audio. They also released a new dataset LibriSpee... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive reading of our work!
> How important is the text-based initialization (using RecurrentGemma-2B) for speechSSM's performance?
Following the insights from Hassid et al. (2023)'s TWIST model, which demonstrated that initializing spoken language models with pretraine... | null | null | null | null | null | null |
Topology-Aware Dynamic Reweighting for Distribution Shifts on Graph | Accept (poster) | Summary: This paper introduces Topology-Aware Dynamic Reweighting (TAR), a novel framework designed to enhance node classification performance under distribution shifts by leveraging graph topology. Unlike invariant learning approaches that rely on strict assumptions about environment labels, TAR applies a dynamic samp... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review and insightful feedback. Below we provide point-by-point responses to address all raised concerns.
# Performance when there is no shift during testing
Thank you for your insightful suggestions!
We compare TAR with several typical robust optimization me... | Summary: This paper proposes a Topology-Aware Dynamic Reweighting (TAR) framework to address distribution shifts in node classification tasks using Graph Neural Networks (GNNs). Addressing the limitations of existing invariant learning methods (which rely on strong invariance assumptions) and sample reweighting approac... | Rebuttal 1:
Rebuttal: We sincerely appreciate your careful review and insightful feedback. Below we provide point-by-point responses to address all raised concerns.
# Analysis of Graph Extrapolation
## 1.Interaction between GE and Reweighting
Graph Extrapolation (GE) is a crucial component of TAR. Without GE, TAR rewe... | Summary: This paper proposes TAR, a framework which dynamically weights and reweights nodes within a Graph-Neural-Network (GNN) given the “risk”-level of nodes, incorporating topological structural information and providing robustness against shifts of distribution.
Claims And Evidence: Claims are backed by theoretica... | Rebuttal 1:
Rebuttal: Thank you for the insights in the evaluation and the hints for revising manuscripts.
> Graph extrapolation could have been mentioned earlier in the paper.
Thank you for your suggestion. We will add a summary in the methodology section to introduce GE earlier.
Once again, thank you for your care... | null | null | null | null | null | null | null | null |
Preference Controllable Reinforcement Learning with Advanced Multi-Objective Optimization | Accept (poster) | Summary: The paper introduces a novel framework, Preference Controllable Reinforcement Learning (PCRL), which trains a single, preference-conditioned policy capable of generating Pareto optimal solutions according to user-specified trade-offs. The approach leverages advanced multi-objective optimization (MOO) technique... | Rebuttal 1:
Rebuttal: Dear reviewer,
We sincerely thank you for your valuable review and constructive feedback.
We address your concerns and aswer your questions below:
---
> Q1. Can the authors provide ablation studies on the similarity weight $\lambda$ to illustrate its impact on both convergence speed and final ... | Summary: The paper proposes a novel approach to learning the ϵ-Pareto efficient frontier in multi-objective optimization using standard reinforcement learning algorithms, specifically TD3 and PPO. The authors introduce a method where preferences are sampled uniformly, and a similarity function between the preference an... | Rebuttal 1:
Rebuttal: Dear reviewer,
We sincerely thank you for your valuable review and constructive feedback.
We address your concerns and aswer your questions below:
---
>**C1: About Figure 4b and 4d:** The empirical results do not consistently ... in Figure 4b and 4d.
**R1:** Figures 4b and 4d are **2D project... | Summary: This paper proposes PCRL for preference control of multi-objective trade-offs, incorporating recent MOO algorithms into MORL. Convergence analysis is provided to show the approach can learn preference-specific Pareto optimal solutions and handles tochastic gradients as well.
Claims And Evidence: Yes, the pape... | Rebuttal 1:
Rebuttal: Dear reviewer,
We sincerely thank you for your time and valuable review.
---
# Response to your questions
Below are our answers to your questions
> Q1. About Theorem 4.2's assumption
**A1:** Theorem 4.2 considers the case where $\lambda$ increases indefinitely. **In practice, Theorem 4.1 alre... | Summary: This paper addresses the limited controllability and coverage of Pareto-optimal solutions in Multi-Objective Reinforcement Learning (MORL), where existing methods based on linear scalarization might struggle to align with user-defined trade-offs and fail to explore the full Pareto front. To overcome these limi... | Rebuttal 1:
Rebuttal: Dear reviewer,
We sincerely thank you for your time and detailed review. We hope the responses below address your concerns.
---
# Answers To Your Questions
---
**A1:** In practical deep RL/MORL, first-order gradient-based algorithms are the most widely-used and stationarity is the stronges... | null | null | null | null | null | null |
TimeFilter: Patch-Specific Spatial-Temporal Graph Filtration for Time Series Forecasting | Accept (poster) | Summary: This paper introduces a novel dependency modeling strategy for time series forecasting, distinct from the commonly used channel independent (CI) and channel dependent approaches (CD). The proposed Patch-wise Filtration method balances CI and CD, allowing for fine-grained and dynamic modeling of spatiotemporal ... | Rebuttal 1:
Rebuttal: Thank you for your insightful advice for polishing our manuscript. We have conducted sufficient experiments and analysis to dispel your concerns. The details can be found below.
## Other Strengths And Weaknesses
---
**`W1`: Alalysis of theoretical complexity.**
**`R1`:** We include a detailed c... | Summary: This paper proposes a Patch-wise filtering modeling approach to select important dependencies and remove irrelevant noisy relationships. It integrates the benefits of CI and CD strategies and offers a more fine-grained and adaptive consideration of dynamically evolving dependencies over time compared to the CC... | Rebuttal 1:
Rebuttal: Thank you for your insightful advice. Here are responses to your questions:
## Other Strengths And Weaknesses
**`W1`: Some modules exhibit limited novelty. For instance, the idea of Dynamic Expert Allocation is not first proposed in this paper.**
**`R1`:** We sincerely appreciate your deep expe... | Summary: This paper introduces a novel approach that addresses the limitations of channel-independent (CI) and channel-dependent (CD) and channel-claustering (CC) strategies. The proposed TimeFilter transition from previous coarse-grained, channel-wise clustering approaches
to a finer-grained, patch-wise partitioning s... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Here are detailed responses to your questions:
## Claims And Evidence
**E1:** Claims of appropriate dependency modeling strategies.
**R1:** We conducted ablation studies in the table below with variants: tem.-only (T), spa.-only (S), spa.-tem.-only (ST), and th... | Summary: In this paper, the authors propose to imporve multivariate time series (MTS) forecasting by proposing the TimeFilter framework, which introduces patch-specific spatial-temporal graph filtration to model dynamic dependencies. Traditional MTS forecasting approaches either follow a channel-independent (CI) approa... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. Here are responses to your insightful concerns and questions:
---
**`Q1`: Novelty and Contribution. (The proposed idea is conceptually similar to existing Granger causality-based methods [1, 2] and instantaneous time series [3], yet the authors fail to discuss t... | null | null | null | null | null | null |
Low-distortion and GPU-compatible Tree Embeddings in Hyperbolic Space | Accept (poster) | Summary: The Authors propose a pipeline to embed trees in hyperbolic space with minimal distortion yet with the ability to operate on accelerated GPU by the generalised Dalaunay embedding algorithm with minimal angle maximization and providing a solid framework to essentially increase the precision by using multiple fl... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind remarks regarding the contribution of our paper and their recognition of the convincing nature of our method. Below we address the main concern of the reviewer, namely the potential and relevance to the machine learning community.
**Relevance to the machine le... | Summary: Existing combinatorial approaches to embedding trees in hyperbolic space suffer from issues stemming from (1) the difficulty of spreading out points on a hypersphere, and (2) floating-point precision issues. To address issue (1), the authors propose highly-separated Delauney tree embeddings (HS-DTE), which max... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback regarding the writing, motivation and results. Below we address the points and questions raised by the reviewer.
**Runtime comparison and memory usage.**
Given the similar theoretical complexities, we consider benchmarking this to be an interestin... | Summary: This paper takes on the task of transforming into an algorithm some mathematical results about low-distortion embeddability of any metric tree into hyperbolic space, whose existence and non-quantitative construction was known by work of Sarkar.
This endeavor leads to two new challenges.
(1) the tree embedding... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful and constructive feedback. Below, we describe how the feedback has been incorporated into the paper and address the reviewer's concerns.
**The importance of embedding given hierarchies in deep learning.**
Many works have shown the strong potential of deep l... | Summary: This paper proposes a construction based tree embedding method in hyperbolic space.
Claims And Evidence: "While these approaches are flexible due to minimal assumptions, the optimization can be unstable, slow and result in heavily distorted embeddings"
This is not true, many hyperbolic embeddings achive grea... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback on the writing and clarity, and we are glad to hear that they like the paper. The reviewer points out four points of discussion and suggestions: the performance of optimization-based methods, experimental results on real-world data, comparison w.r.... | null | null | null | null | null | null |
Statistical Collusion by Collectives on Learning Platforms | Accept (oral) | Summary: This paper introduces a framework in which a group of users, or “collective,” can statistically coordinate modifications to their data to influence a platform’s learning algorithm. The authors define several types of objectives—“signal planting,” “signal unplanting,” and “signal erasing”—and provide theoretica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and appreciate their positive feedback on our paper. | Summary: The paper proposes a framework to quantify the outcome of collective actions in machine learning, performed through the coordinated submission of altered data. In particular, the proposed framework allows the derivation of lower bounds on the success of the collective's strategy. The authors advocate for strat... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed and constructive review, and their overall positive feedback. We also appreciate them pointing out the typos. We will update $n_{est}$ to $n_{estim}$, add assumption $A_0$ regarding the collective's access to $N$, and include a running exampl... | Summary: This paper addresses the problem of collective action under finite samples. There are $N$ consumers each with a datapoint $(x,y)\sim \mathcal{D}$ where $x\in \mathcal{X}$ and $y\in \mathcal{Y}$. Of these $N$ consumers $n$ consumers plan to collude, so that after learning from the $N-n$ clean samples and $n$ co... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed and technical review, which allows for a discussion of some of the paper’s theoretical aspects. We also appreciate their positive feedback and will now address the questions raised.
*Better empirical performance than (Hardt et al 2023) in Fi... | Summary: The paper explores the statistical inference for collective action in learning platforms, in particular, the paper examines not only the classic signal planting procedure introduced by Hardt et al, but also signal unplanting as well as signal erasing, both of which require statistical inferences for defining t... | Rebuttal 1:
Rebuttal: First, we would like to thank the reviewer for their time and their insightful comments. As mentioned, our main contribution is introducing a statistical inference component to the collective action framework, which is crucial in practice, as collectives often seek guarantees about the effectivene... | null | null | null | null | null | null |
Origin Identification for Text-Guided Image-to-Image Diffusion Models | Accept (poster) | Summary: This paper introduces a new task, identifying the original image for a generated image from text-guided image-to-image translation with diffusion models, which helps prevent the misuse of the generated content such as misinformation and copyright infringement. To deal with this problem, the authors build a dat... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper. We are encouraged that you find: (1) Most of claims are supported by **clear** and **convincing** evidence; (2) this paper proposes an **important**, **novel**, and **interesting** task; (3) the analysis appears **thorough** and **compre... | Summary: This paper introduces the Origin Identification for Text-Guided Image-to-Image Diffusion Models (ID^2) task, aiming to retrieve the original image of a given translated query. The paper highlights the risks of misuse, including misinformation, copyright infringement, and evading content tracing. A key contribu... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper. We are encouraged that you find: (1) the proposed linear transformation approach is **simple**, **efficient**, and **theoretically grounded**; (2) OriPID dataset provides a **strong benchmark** for **future work** on ID$^2$; (3) Extensiv... | Summary: - This paper introduces a new problem, "origin identification", for text‐guided image‐to‐image diffusion models, with the goal of retrieval the original image given a query image that was transformed by a text‐conditioned diffusion model.
- The paper proposes a new dataset OriPID, containing original images, t... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper. We are encouraged that you find our work (1) provides a **large-scale** and **carefully curated** benchmark, (2) proposes a **novel** retrieval-based method, (3) includes **theoretical** arguments, (4) is **intuitive** and **make sense**... | Summary: This paper introduces the ''Origin Identification'' task for text-guided image-to-image diffusion models, aiming to retrieve the original image of a given modified image generated by diffusion models. The motivation for this task stems from security concerns, including misinformation, copyright infringement, a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts in reviewing our paper. We are encouraged that you find: (1) this paper's claims are **well-supported**; (2) the proposed methods and evaluation criteria are **well-aligned** with the origin identification task; (3) the experimental design is generally **well-s... | null | null | null | null | null | null |
HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinking | Accept (poster) | Summary: This paper proposes a tree-based planning strategy, called **HyperTree Planning (HTP)**, which utilizes a hypertree-structured planning framework. HTP adopts a divide-and-conquer approach, decomposing a complex goal into several sub-goals in a top-down manner. These sub-goals are further decomposed iteratively... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful, valuable, and positive comments. We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns.
### Weakness 1
>discussion on cost concern and potential improvements.
We will incorporate **a discuss... | Summary: This paper concentrates on complex planning tasks, for instance, mathematical and logical reasoning. To alleviate the multiple challenges, extended reasoning steps, diverse constraints, and multiple distinct sub-tasks, they propose a HyperTree Planning (HTP) that is based on the divide-and-conquer strategy to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful, valuable, and positive comments. We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns.
### Weakness 1
>Do humans almost never make errors across multiple complex tasks?
In **TravelPlanner**... | Summary: This paper introduces HyperTree Planning (HTP), a reasoning paradigm designed to improve complex planning tasks using hierarchical hypertree-structure.
Claims And Evidence: The core motivation is that existing reasoning methods (e.g., CoT, ToT) struggle with long-horizon, multi-constraint planning problems, s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful, valuable, and positive comments. We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns.
### Weaknesses 1
>It is beneficial to include further analyses in terms of efficiency metrics.
We will... | Summary: This paper proposes an autonomous planning framework called HyperTree Planning that involves (a) HyperTree Constrution, (b) Self-Guided Planning and (c) Plan Generation. It tackes the limitation of exisiting chain-of-thought and tree-of-thought on planning problems, for example, they focus on mathematical and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful, valuable, and positive comments. We address the concerns in detail as follows. We sincerely hope that our response could properly address your concerns. If so, we would deeply appreciate it if you could raise your score. If not, please let us know your fur... | null | null | null | null | null | null |
Towards Understanding Fine-Tuning Mechanisms of LLMs via Circuit Analysis | Accept (poster) | Summary: This paper investigates the mechanisms of fine-tuning in LLMs through circuit analysis, focusing on mathematical tasks where pre-trained models perform poorly but improve significantly after fine-tuning. The authors identify that edge modifications in circuits (rather than node changes) drive performance gains... | Rebuttal 1:
Rebuttal: Thanks for your review and helpful suggestions! These are good points, which we answer below.
>Q1: I'm curious if you combine the discovered circuit for the single task, whether the combined circuit can perform the compositional task like you measure the faithfulness.
Based on your suggestion, we... | Summary: * The paper investigates circuits in LLMs (subsets of the computational graph) that have been finetuned to complete various small mathematical tasks (e.g. add two numbers).
* The paper computes circuits (using standard methods) at different stages in the finetuning process and on different data. After verifyin... | Rebuttal 1:
Rebuttal: Thanks for your helpful review!
>Q1:The authors of this paper will provide a persuasive explanation…, then I would be happy to raise my score.
We apologize for the oversight and have added the missing reference. Both works use EAP-IG as a shared tool, not a core contribution. We clarify key diff... | Summary: The paper studies how fine-tuning works in LLM using the circuit analysis method. It presents a set of mathematical tasks that show clear performance improvements during fine-tuning, unlike previous work that focused on already well-performing pre-trained models. The authors find that fine-tuning mainly change... | Rebuttal 1:
Rebuttal: Thanks for your review and helpful suggestions!
>Q1: This indicates that circuit dynamics play a crucial role…Actaully, I cannot agree with it. The total num of edges is much greater then total number of nodes. The ratio of changement can deliever more information.
To account for this, we use a *... | Summary: The paper studies the dynamics of fine-tuning a LLM on mathematical tasks that the model initially can't perform. This is studied through the lens of circuits identified in an automated manner using edge attributions derived via integrated gradients (where the gradients are presumably derived from the ground-t... | Rebuttal 1:
Rebuttal: Thanks for your review and helpful suggestions!
>Q1: However, this method based on ratios is not a priori an apples-to-apples comparison...the observed higher fraction of edges may be an artifact of these scaling dynamics ...
To further distinguish whether it is the natural expansion of the struc... | null | null | null | null | null | null |
A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Attention Networks | Accept (poster) | Summary: The paper provides a new controlling method to steer away node embeddings falling into oversmoothing state during propagation compared with existing method such as G2-gating. This controlling method specifically target for graph attention based method with the gradual pruning highly correlated connection trick... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and are pleased that our theoretical analysis and overall approach were well received. Below, we address the main concerns:
1. **Limited Empirical Evaluation on Real-World Datasets**
We agree that evaluating on larger-scale datasets is imp... | Summary: This paper introduces a refreshing perspective on the over-smoothing behavior of graph neural networks through the lenses of dynamical systems. After the establishment of the theoretical framework, a novel architecture which dynamically prunes the attention weights has been proposed and achieved state-of-the-a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive assessment and insightful suggestions. Below, we address the key points raised:
1. **Evaluation on Larger and More Realistic Datasets:**
As noted in our response to Reviewer kxJk (see the table provided therein), we have extended our evaluation to larg... | Summary: The paper presents a dynamical systems take on GNNs and proposes dynamically pruning edges based on learnt attention weights in GAT to combat oversmoothing.
## update after rebuttal:
I thank the authors for their detailed response to my questions and their effort in providing further experiments. While resu... | Rebuttal 1:
Rebuttal: **Weakness 1: Limited evaluation on small-scale real-world datasets**
As discussed in our response to Reviewer kxJk (see the table provided therein), we have extended our experiments to larger-scale benchmarks such as ogbn-arxiv and ogbn-products. These additional results confirm that DYNAMO-... | Summary: The paper introduces **DYNAMO-GAT**, a **pruning strategy** for **Graph Attention Networks (GATs)** to mitigate **oversmoothing** using a **dynamical systems perspective**. The authors propose:
1. **Noise-driven covariance analysis** to detect oversmoothing.
2. **Anti-Hebbian learning** to selectively prune at... | Rebuttal 1:
Rebuttal: 1. **Performance on Heterophilic/Dense Graphs:** Our theory—based on spectral gap & fixed-point stability—does not assume homophily or sparsity, generalizing across diverse graph types.
* Empirical validation:
* Fig. 3 (Syn-Products): DYNAMO-GAT (DGAT) DGAT performs well as edge density increase... | null | null | null | null | null | null |
Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via Topology Alignment | Accept (poster) | Summary: This paper proposes the Multi-Domain Graph Foundation Model (MDGFM) to address the challenge of transferring knowledge across graphs from different domains. MDGFM aligns graph topologies through a decoupled embedding mechanism, a graph structure learning module, and a prompt-tuning approach. This alignment all... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. Your constructive criticism is invaluable in refining our work. Below, we give point-by-point responses to your comments.
**Weakness 1 & Question 1: Further explanations**
Thank you for raising this important point. We agree that a clearer explanation of ... | Summary: The authors propose MDGFM to solve the graph pre-training issue. The key contributions include: A novel framework that aligns graph topologies across multiple domains using Graph Structure Learning (GSL);An adaptive embedding mechanism that balances features and topologies for improved generalization; A dual-p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive and constructive feedback. We greatly appreciate your recognition of the importance of the problem, the motivation of our proposed components, and the comprehensive empirical evaluation. Below we address your valuable suggestions and questions.
**W... | Summary: The authors propose a unified approach that aligns graph topologies and features across domains, leveraging Graph Structure Learning (GSL) to refine noisy and adversarial-prone real-world graphs. The framework also introduces an efficient prompt-tuning mechanism to enhance knowledge transfer to unseen domains.... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the highly encouraging and constructive feedback. We are especially grateful for your recognition of our model’s generalization capability, methodological contributions, and comprehensive evaluations. Below we address your valuable suggestions.
**Weakness: Intu... | null | null | null | null | null | null | null | null |
Rank-One Modified Value Iteration | Accept (poster) | Summary: The authors propose a novel algorithm for solving planning and learning problems of Markov decision processes.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: Yes, to some extent.
Experimental Designs Or Analyses: Yes, to some extent.
Supplementary Material: No.
Relati... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments/questions. Here is our response:
__R1. Equation (2):__ The first line of eq. (2) is the definition of the value $v^{\pi}$ of a control policy $\pi:\mathcal{S}\rightarrow\mathcal{A}$ as the expected, discounted cost endured by following policy $\pi$:
$$
v^{\p... | Summary: This paper proposes a rank-one modified value iteration algorithm which is a modified policy iteration which approximates the transition dynamics by a rank-one update. The authors prove formal convergence guarantees and demonstrate the algorithm's empirical potential through numerical simulations.
Claims And ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments/questions. Here is our response:
__R1. Convergence of PI:__ The reviewer is right. In the worst case, the convergence of PI in the value space is similar to VI, which is linear with rate $\gamma$ [4, Thm. 6.4.6]. However, under certain conditions, this conv... | Summary: In this paper, the authors have proposed algorithms for planning and learning in MDP based problems. The proposed algorithms use a rank-one approximation of the transition probability matrix in the policy evaluation step. It uses the stationary distribution of the transition probability matrix, approximated us... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments/questions. Here is our response:
__R1.__ Yes. The proposed algorithms can be extended to the average cost setting. For example, consider the PI algorithm that uses relative VI for policy evaluation for unchains proposed in [4, Sec. 8.6.1]. We have managed to... | Summary: The paper introduces an accelerated value iteration algorithm by using a rank-one approximation of the transition dynamics in policy iteration. They further extend the algorithm to RL and introduce R1-QL. It is theoretically shown that these algorithms converge at least as fast as their conventional counterpar... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments/questions. Here is our response:
__R1. Missing references:__ We thank the reviewer for bringing these papers to our attention; we were not aware of them. We will update the manuscript to include them and the following discussion:
Ref. [1] considers the matr... | null | null | null | null | null | null |
Rejecting Hallucinated State Targets during Planning | Accept (poster) | Summary: This paper addresses the issue of hallucinated state targets in model-based reinforcement learning (MBRL), where generative models can produce unrealistic or unreachable states, leading agents to delusional planning behaviors. Inspired by human cognition, the authors propose an evaluator that assesses the feas... | Rebuttal 1:
Rebuttal: `HOW EVALUATOR PERFORMS IN PARTIALLY OBSERVABLE DOMAINS? WOULD THE APPROACH NATURALLY EXTEND IF THE “SOURCE STATE” IS UNCERTAIN?`
We agree with your intuition that it naturally extends.
The evaluator takes on paired inputs of the source state representation and the target representation, both ar... | Summary: The paper proposes to augment Target-Assisted Planning (TAP) methods with an evaluator to reject generated states that are unfeasible and improve performance. The proposed method is evaluated on two environments: SwordShieldMonster (SSM) and RandDistShift (RDS) with 3 different TAP agents: Dyna, Skipper and L... | Rebuttal 1:
Rebuttal: `WHAT IS THE PERFORMANCE OF SKIPPER & LEAP WHEN THE EVALUATOR IS NOT USED TO REMOVE PREDICTED INFEASIBLE STATES?`
As discussed in Sec. 2 (Line 57 right column), Sec 5.1.2 (L383 left col.) , Sec. 6 (L388 r. col.), these methods already use their own evaluators to remove the targets they think tha... | Summary: The paper analyzes the issue of generating invalid subgoals during planning. The authors categorize different failure modes and propose strategies for learning a classifier that can be used to estimate the distance to a proposed goal, including whether it is reachable at all. Through experimental evaluation in... | Rebuttal 1:
Rebuttal: We reordered the questions to streamline our response.
`Discuss the relation … to [Z et al.] & [A et al.]. Are the targets for “generate” generated during sampling, or …?`
[Z et al.]
Our approach of rejecting infeasible targets is indeed similar to that in [Z et al.]. Yet, the approaches differ... | null | null | null | null | null | null | null | null |
Scalable First-order Method for Certifying Optimal k-Sparse GLMs | Accept (poster) | Summary: In this work, the authors proposed a novel FISTA-based algorithm for computing a lower bound in the Branch-and-Bound method. The new algorithm utilizes several customized components and outperforms universal optimization solvers on both artificial and practical datasets. Please see the following sections for m... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our paper! This is indeed a typo. The implementation is correct in our submitted source code. We will fix this typo in the revision. | Summary: This paper explores the use of branch-and-bound (BnB) frameworks to solve sparsity-constrained optimization problems.
The authors derive a lower bound for the optimization problem using a perspective relaxation formulation.
To efficiently solve the resulting perspective relaxation, the authors employ a first... | Rebuttal 1:
Rebuttal: We thank the reviewer for thoughtful feedbacks.
1. **k is not large enough**
a. *Many real-world datasets are naturally sparse*. Small $k$'s are sufficient for accurate prediction and can help avoid overfitting, especially on the validation set. Small $k$ also improves interpretability. This is... | Summary: This paper studies sparse generalized linear models with cardinality constraints. Existing branch-and-bound methods are not computationally efficient due to expensive or slow dual bound computations. To overcome this, the authors propose a first-order proximal gradient algorithm to solve the perspective relaxa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback.
We briefly restate our contribution to avoid any misunderstanding.
Our paper addresses certifying optimality or quantifying the optimality gap for the $\ell_0$-constrained GLMs, \textbf{without making any assumptions on the data}.
Our motivation ... | Summary: The paper proposes a new algorithm for solving lower bounds in the BnB framework. It solves a composite problem using an efficient restarted FISTA algorithm, for which an efficient way to exactly compute the function value and the proximal operator is given. As such, the paper allows to achieve impressive effi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
1. **Connection to Argyriou et al. and McDonald et al.**
Thank you for these valuable references--we will cite and discuss both papers.
We fully agree with your observations.
We clarify some key differences:
... | Summary: This paper considers the problem of sparse generalized linear model (GLM) optimization. Specifically, the goal is to fit a generalized linear model under the constraint that at most $k$ features are used. Typical approaches for such problem include LASSO, greedy/local search-based techniques, as well as branch... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We address their concerns below:
1. **Connection to Mhenni et al. and experimental comparison**
Thank you; we will cite this paper. However, we note that our perspective relaxation is tighter than the $\ell_1$ relaxation from by Mhenni et al.
... | null | null | null | null |
Deep Neural Cellular Potts Models | Accept (poster) | Summary: The paper introduces NeuralCPM, a novel cellular Potts model (CPM) that employs a neural network to parameterize the Hamiltonian, diverging from traditional CPMs that rely on manually-defined, physics-inspired analytical Hamiltonians.
This Neural Hamiltonian is designed to respect symmetries inherent in cellu... | Rebuttal 1:
Rebuttal: Thank you for the rigorous review. We are happy to learn that you appreciated the originality, significance and clarity of our work.
We address your specific comments in our response below;
any new tables and figures (referred to as e.g. Figure R1) can be found via https://anonymous.4open.science... | Summary: In this thesis, we propose NeuralCPM, a neural network-based cellular Potts model (CPM), which aims to learn the dynamics of multicellular systems through neural Hamiltonian. The core idea of the method is to use Deep Energy-Based Model (EBM) to train Hamiltonian in order to overcome the problem of traditional... | Rebuttal 1:
Rebuttal: Thank you for the thorough review! We appreciate that you found our methodology clear, innovative, and rigorous.
We address your specific comments in our response below; any new tables and figures (referenced as e.g. Figure R1)
can be found via https://anonymous.4open.science/r/neuralcpm-rebuttal-... | Summary: This paper introduces Neural Hamiltonians, a novel approach for parameterizing the Hamiltonian function in cellular Potts models. The authors propose using neural networks to learn the Hamiltonian function directly from data, while preserving important physical and biological constraints.
The key innovation i... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We are glad that you found our work well-written and sound, and that you appreciated the innovations of our approach.
Our response can be found below; new results (referenced as e.g. Figure R1)
can be found via https://anonymous.4open.science/r/neuralcpm-rebu... | Summary: This paper introduces NeuralCPM, a neural network-based approach to cellular Potts modeling (CPM) for simulating collective cell dynamics. Traditional CPMs rely on physics-inspired Hamiltonians that require significant domain expertise to design and may not fully capture complex biological behaviors. NeuralCPM... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and valuable suggestions.
We are glad to hear that you found our Neural Hamiltonian well-designed and that you appreciated the validation against real biological data.
We address all comments and questions of your review below;
new tables and figures (referred to ... | null | null | null | null | null | null |
Prediction-Powered Adaptive Shrinkage Estimation | Accept (poster) | Summary: This paper proposes a "shrinkage estimator" for estimating multiple problems at once using PPI (prediction powered inference). They discuss the various methods by which one can reduce variance in such an estimator and how their method takes advantage of each, demonstrating theoretically that they can get bette... | Rebuttal 1:
Rebuttal: We thank the reviewer for considering our idea convincing and thoughtful comments.
* **Sharing information across problems:** We agree with the reviewer that further motivation of why and how it is possible to share information across problems will improve our exposition. In our revision we will ... | Summary: This paper proposes a method for adaptively combining ML predictions with gold-standard labels, to estimate a multivariate parameter (e.g., the mean across several partitions of the data) with small mean-squared error. The paper builds upon the PPI++ estimator, while proposing to additionally perform global s... | Rebuttal 1:
Rebuttal: We thank the reviewer for a very helpful report, which has helped us improve on this work.
* We agree that we should expand on connections to related work, which will be added as a new section in the appendix after our revision. Briefly:
- Fisch et al. 2024 (StratPPI): The starting point is s... | Summary: The paper proposes the Prediction-Powered Adaptive Shrinkage (PAS) method to enhance estimation accuracy for multiple means. PAS integrates Prediction-Powered Inference (PPI) with empirical Bayes shrinkage, first debiasing noisy machine learning (ML) predictions within each task and then leveraging information... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty and contribution in our paper, as well as the many constructive comments. We hope our response below addresses all the concerns directly.
### **Background on PPI**
The core idea of Prediction-Powered Inference (PPI) is as follows. Given existin... | Summary: Paper proposes prediction-powered adaptive shrinkage (PAS), an extension of prediction-powered inference (PPI) that uses empirical Bayes ideas to further reduce estimates when multiple related estimation problems are solved together. The paper is well written and well thought out, with good theoretical and emp... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful assessment. The thoughtful questions have helped us improve our work. For better exposition, we slightly reordered our responses to the questions.
1. **Finite $m$ results.** Our current theoretical analysis permits for finite sample bounds. We will track thes... | null | null | null | null | null | null |
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning | Accept (poster) | Summary: The paper introduces a novel framework for multi-fidelity active learning that is based on learning of hierarchical latent representations. The authors evaluate the approach in a multi-fidelity setting culminating in ABFE evaluation on two different protein targets.
Claims And Evidence: The claims are support... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and positive comments about the work.
> **Q1:** Because the authors opt to use a setting close to real-life, with MD as the highest-fidelity oracle, they are limited to sampling very few samples in the end (15 per method). This is somehow understan... | Summary: This paper introduces Multi-Fidelity Latent space Active Learning(MF-LAL), an framework that integrates a set of different oracle functions to guide the generation of molecules to get higher predicted activity. It combines the generative model and surrogate model into a single framework, and the computational... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and positive comments about the work.
> **Q1:** In table 1, POCKET2MOL and MF-LAL generated 40 molecules while other baseline methods generated 15 molecules. It seems that this is unfair as generating more molecules would definitely result in bette... | Summary: This paper introduces a new approach for generating drug candidates, called MF-LAL. The proposed method utilizes a variational autoencoder with multiple latent spaces arranged hierarchically to accommodate different fidelities. The first level employs a regression model trained to predict activity on known com... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and positive comments about the work.
> **Q1:** There are no details on how GCN was implemented for generating molecules.
**A1:** We will include details of our GCN implementation in the Appendix in the updated draft. Briefly, we used a three-laye... | Summary: This paper introduces, MF-LAL, a generative algorithm for drug discovery based on biological activity rather than docking. Rather than conditioning on molecular docking, the authors propose a pipeline to generate molecules based on molecular dynamics-based binding free energy. As MD-based free energy calculat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback and positive comments about the work.
> **Q1:** The training details including the supplement are sparse and disjointed. It is not easy to understand how exactly the model is trained. I know space is limited, but even an algorithm in the supplement... | null | null | null | null | null | null |
Off-Policy Actor-Critic for Adversarial Observation Robustness: Virtual Alternative Training via Symmetric Policy Evaluation | Accept (poster) | Summary: The paper presents a novel off-policy reinforcement learning approach that addresses adversarial input observations without requiring additional environmental interactions, thus enhancing sample efficiency and avoiding inefficiencies in agent-environment interactions. By reformulating adversarial learning as a... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and detailed feedback. Below, we respond to each point.
---
## Methods and Evaluation Criteria
### About Baseline [1]
> [1] Reddi et al., *Robust adversarial reinforcement learning via bounded rationality curricula*, ICLR 2024
Thank you for the sugges... | Summary: This paper proposes a method to address the observation robustness, which does not rely on interacting with the environment and making the algorithm off-policy.
Claims And Evidence: **General:**
By looking at the formulation in sec. 3 and related work, it seems this work aims only at state adversarial robust... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and detailed feedback. Below, we respond to each of the concerns and suggestions.
---
## Claims and Evidence
### **Scope Clarification (Title/Abstract/Introduction)**
Thank you for pointing this out. We agree that our work specifically address... | Summary: This paper proposed an off-policy VALT framework for SA-MDP based on Symmetric Property and Soft Optimization. Compared with the existing ATLA framework, this framework improves sample utilization efficiency as it does not require additional training for the Adversary.
Claims And Evidence: + In Line 50, it is... | Rebuttal 1:
Rebuttal: We appreciate your review and interest in our work.
Below, we address your main concerns. We also acknowledge that some notational details may have been omitted due to space limitations.
---
## Claims and Evidence
### **Motivation and Advantage of this work**
We believe investigating off-polic... | Summary: The paper "Robust Off-Policy Actor-Critic: Virtual Alternative Training via Symmetric Policy Evaluation" addresses the challenge of training reinforcement learning (RL) agents that are robust to adversarial perturbations in their input observations. Existing methods often rely on alternating training between t... | Rebuttal 1:
Rebuttal: **We appreciate your detailed review and insightful suggestions.**
We understand that your main concerns center around:
(1) adaptation to discrete action domains,
(2) analysis of computational cost, and
(3) performance degradation of VALT on HalfCheetah in the absence of noise.
Below, we... | null | null | null | null | null | null |
AlphaQCM: Alpha Discovery in Finance with Distributional Reinforcement Learning | Accept (poster) | Summary: The paper introduces AlphaQCM, a novel reinforcement learning method for discovering formulaic alphas in finance. It conceptualizes alpha discovery as a non-stationary and reward-sparse Markov decision process and addresses challenges through a Q-learning framework combined with quantile-based variance estimat... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback and helpful comments. To improve the quality of this paper, we have carefully considered your suggestions and questions. Limited by the max length, our shorten replies are as follows:
## Q1
*The claim that AlphaQCM outperforms AlphaGen is supported by results ... | Summary: The authors propose a method based on distributional reinforcement learning and QCM to learn a good set of well-formed features (i.e. alphas) for stock market prediction. The proposed method is compared with existing baselines and is shown to outperform these baselines on 3 stock market datasets.
## Update af... | Rebuttal 1:
Rebuttal: Thank you for the positive review and very helpful feedback. Please let us know whether the points below are sufficient clarification for your concerns.
---
## Q1 from "Theoretical Claims":
*There is not much theoretical result in this work except the consistency of the moment estimator under s... | Summary: This paper introduces AlphaQCM, a novel distributional reinforcement learning (DRL) method for discovering synergistic formulaic alphas in finance. The authors conceptualize the alpha discovery process as a non-stationary and reward-sparse Markov Decision Process (MDP) and propose AlphaQCM to address these cha... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and positive attitude towards our work. To enhance the quality of this paper, we have carefully considered your suggestions and questions. Below, we provide a point-by-point response to your comments:
---
## Q1 from "Methods And Evaluation Criteria":
*Howev... | Summary: This paper proposes a distributional reinforcement learning-based alpha discovery process for algorithmic trading in the stock market. Motivated by the quantile conditional moments (QCM) method, the authors provide an unbiased estimation of variance from quantiles to improve the performance of discovering syne... | Rebuttal 1:
Rebuttal: Thank you very much for your insightful comments and suggestions, which give us a great help to improve our article in the future. Limited by the max length, we hope you will find that our responses have successfully addressed the important issues you have raised.
---
## Q1
*The authors present ... | null | null | null | null | null | null |
Certified Unlearning for Neural Networks | Accept (poster) | Summary: This paper proposes to analyze the formal unlearning guarantees of two varieties of clipped noisy finetuning (either model or gradient clipping) by using recent post-processing DP analyses. Specifically they propose to first project and add noise to the original model, and then apply $T$ steps of clipped (eith... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable comments that will allow us to improve our manuscript.
### **Clarification on Definition 2.1:**
We apologize for the confusion caused. We believe that the reviewer misinterpreted our definition. We would like to note that **no modification to the training a... | Summary: The paper presents a post processing technique to guarantee approximate unlearning in non-convex settings. The paper builds on several works in the differential privacy literature that incorporated noise during the optimization process to improve privacy guarantees. The paper proposes two methods: gradient cli... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive feedback.
1. We acknowledge that our experiments currently focus on fundamental settings (MNIST, CIFAR10), yet our primary contribution lies in providing rigorous theoretical guarantees without restrictive assumptions such as smoothness or con... | Summary: The paper proposes a novel certified unlearning method that integrates noisy fine-tuning with privacy amplification by stochatic post-processing, which introduces gradient clipping and model clipping, both combined with Gaussian privacy noise. The authors provide rigorous theoretical analysis for unlearning gu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and constructive feedback.
1. Thanks for pointing to the papers about unlearning for graph neural networks and minmax models. We will add these references to the related works section.
2. We acknowledge that our experiments currently focus on fundamental sett... | Summary: **Main Results**: Although the idea of fine-tuning an originally trained model on retained data has been proposed before, it has traditionally been viewed as an empirical forgetting strategy for non-convex tasks. This paper provides certified unlearning guarantees for neural networks without requiring knowledg... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback, and we address the key points below.
**1. Baseline Comparisons:**
We choose only output perturbation and retraining from scratch since these algorithms are the only baselines in the literature that can achieve certified unlearnin... | null | null | null | null | null | null |
RE-Bench: Evaluating Frontier AI R&D Capabilities of Language Model Agents against Human Experts | Accept (spotlight poster) | Summary: The authors provide 7 research engineering problems together with surrounding environments. They evaluate both humans and AI models (Claude Sonnet 3.5, o1) on these problems, over different amounts of time spent and solution attempts. The results show that over short time horizons, the AI models tend to perfor... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review and insightful comments\! We are glad that the reviewer finds the work to be well-written, the evidence to be clear and convincing, and that the results provide insight into the capabilities of current frontier AI on realistic research engineering.
W... | Summary: This paper contributes a new LLM (Agent) benchmark **RE-Bench**, consisting of 7 ML research engineering tasks for evaluating whether AI agents can autonomously perform AI R&D. A human study is also conducted, and results are analyzed.
## update after rebuttal
I thank the authors for their rebuttal. I mainta... | Rebuttal 1:
Rebuttal: We are pleased to hear that the reviewer found the paper is well-written and clear to read, RE-Bench is novel and original, and the contributions significant. We address the questions the reviewer had below and we’d be happy to provide any further clarifications.
Q1: How were “cheating” or enviro... | Summary: In this work, the authors propose RE-Bench, which is designed to assess the capabilities of AI agents for AI research and development, especially in comparison with human experts. They define 7 ML engineering environments with scoring functions and evaluate human experts and AI agents on those under the same t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, attention and thoughtful feedback. We are glad that the reviewer finds the RE-Bench tasks to be nontrivial assessments of research engineering. We appreciate that the reviewer found the paper and presentation of the work to be high quality and the experimental... | null | null | null | null | null | null | null | null |
A First-order Generative Bilevel Optimization Framework for Diffusion Models | Accept (poster) | Summary: The authors have proposed a bilevel optimization framework tailored for diffusion models, specifically addressing two scenarios:-
Fine Tuning a pre-trained diffusion models (via Inference-only Solver):
To fine-tune pre-trained diffusion models, to maximize the task-specific rewards, while preserving the aesth... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the theoretical innovation and our algorithm design. Our response to your comments follows.
**Q1. Baseline comparison.** Thank you for your question. We have added numerical comparisons using different reward functions during the rebuttal period; please see... | Summary: This paper explores the application of bilevel optimization in diffusion models, focusing on two key applications. The first optimizes the trade-off parameter that balances the reward and proximity to the pre-trained distribution during fine-tuning. The second optimizes the noise schedule in diffusion models. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the theoretical guarantees for our algorithm and its empirical performance. Our response to your comments follows.
**Q1. Optimization merely on $x$ in equation 1?**
Thank you for your question. In equation 1, we state the general bilevel HPO formulation w... | Summary: This paper introduces a practical, first-order bilevel framework for diffusion models, outperforming standard methods in fine-tuning and training scenarios. The proposed method eliminates the high dimensionality and sampling costs in traditional methods.
Claims And Evidence: The claims are supported by experi... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our novelty. We hope our response to your comments below can resolve your minor concerns.
**Q1. Insufficient numerical experiments.**
- **One hyperparameter in the fine-tuning diffusion model experiment.** Although KL regularization is just one hyperparam... | Summary: The paper explores the problem of bilevel optimization with diffusion models - a hierarchical framework consisting of a higher and lower level objectives which are jointly optimized. The authors frame the following two problems as bilevel optimization:
1. KL regularized reward maximization as the lower level o... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our topic. We hope our response to your comments below can resolve your minor concerns.
**Q1. Motivation of two hyperparameter optimization (HPO) problems.**
In the first application, the primary concern is whether adding additional computational cost for... | null | null | null | null | null | null |
Adversarial Perturbations Are Formed by Iteratively Learning Linear Combinations of the Right Singular Vectors of the Adversarial Jacobian | Accept (poster) | Summary: This paper introduces RisingAttack, a novel method for generating ordered top-K adversarial attacks on Deep Neural Networks (DNNs) by optimizing directly in the image space. The method leverages Sequential Quadratic Programming (SQP) to manipulate adversarial perturbations as linear combinations of the right s... | Rebuttal 1:
Rebuttal: Dear Reviewer D2j5,
Thank you for your valuable comments and efforts. We address your concerns as follows and will update those carefully in the revision.
### C1: The reliance on computationally intensive techniques like SQP and SVD may render RisingAttack less feasible for real-time or on-dev... | Summary: In this work, the authors propose an ordered top-K targeted white-box attack called RisingAttacK by solving the non-linearly constrained optimization problem in image space under the sequential quadratic programming framework. Experiments on ImageNet-1k dataset validate the effectiveness of RisingAttacK.
Clai... | Rebuttal 1:
Rebuttal: Dear Reviewer 69zR,
Thank you for valuable comments and efforts. We address your concerns one by one as follows. We will carefully update those in the revision.
### C1: I cannot figure out why we need the top-K targeted attack. Can you provide any practical scenarios or its benefits compared ... | Summary: The paper introduces a new method for generating ordered top-K adversarial attacks. The authors use Sequential Quadratic Programming (SQP) to solve the optimization problem behind top-K adversarial attacks directly in the image space. After adapting the SQP algorithm to make the computation tractable and avoid... | Rebuttal 1:
Rebuttal: Dear Reviewer ubo3,
Thank you very much for your valuable comments and efforts. We address your concerns one by one as follows, which will be carefully updated in revision.
### C1: Report the time complexity of an iteration of RisingAttacK and that of QuadAttacK
> Thank you. We report the **a... | null | null | null | null | null | null | null | null |
CVE-Bench: A Benchmark for AI Agents’ Ability to Exploit Real-World Web Application Vulnerabilities | Accept (spotlight poster) | Summary: This paper presents CVE-Bench, a new benchmark designed to evaluate AI agents in the cybersecurity domain, specifically focusing on real-world web vulnerabilities. It compiles 40 CVEs from the past year, covering eight attack types, to create a comprehensive assessment framework. To simulate realistic exploita... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We will incorporate the suggestions in the revision.
> **E1**: Using the same environment for each agent - for example, install sqlmap in the container of CyAgent and not only for the T-Agent.
We clarify that sqlmap is installed in the contain... | Summary: 1. This paper proposed a new benchmark for LLM-Agent Attacking.
2. Some experiments are conducted.
Claims And Evidence: **Yes**
Methods And Evaluation Criteria: **Yes**
There is no proposed method in this paper. Only the new CVE-Bench is intorduced. The authors provide a comparision with other similar bench... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We will incorporate the suggestions in the revision.
> W1: The presentation of this paper should be improved:
> (Minor) In Figure 3, the Y-axis should display "30%" rather than simply "30" to properly indicate percentage values.
> Figure 4 la... | Summary: The authors introduce CVE-Bench, a new benchmark designed to evaluate large language model (LLM) agents’ capabilities in identifying core cybersecurity vulnerabilities. They define 8 key types of core attacks that any robust system should withstand. This benchmark significantly reduces manual effort by enablin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. We provide the following clarifications and will include them in the revision.
>Even in the 1-day setting, agents are entirely unable to solve tasks. Analysis of failure modes is suggested.
We provide a quantitative analysis of failure modes in... | null | null | null | null | null | null | null | null |
HaploVL: A Single-Transformer Baseline for Multi-Modal Understanding | Accept (poster) | Summary: - This paper introduces HaploVL, a multimodal model with a dual Transformer decoder for joint vision-language processing.
- The proposed two-stage training distills knowledge from a pre-trained model into the first decoder, integrating text and vision.
- HaploVL extends the EVE approach by reusing aligned visi... | Rebuttal 1:
Rebuttal: We appreciate your suggestions and feedback. In the following, we respond to the major concerns.
* Q1: This early-fusion design is conceptually similar to related work such as EVE, offering only minor innovations over EVE when combined with a customized training recipe.
**Response:** We distingu... | Summary: HaploVL is a large, single-transformer multi-modal model designed to overcome the limitations of existing models by integrating visual and textual inputs early on for efficient multi-modal comprehension.
They introduce an innovative pre-decoder model that merges visual patches with text embeddings at the init... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and feedback. We respond to the major concerns in the following.
* Q1: The comparison method is not detailed enough.
**Response:** Due to page limitations, we have compared our model against several widely-recognized methods, which include very mainstream approache... | Summary: The paper introduces HaploVL, an early-fusion multi-modal model (LMM) that processes visual and textual inputs through a single-transformer architecture. Unlike traditional compositional LMMs that handle modalities separately, HaploVL integrates raw visual and textual embeddings at an early stage, leveraging a... | Rebuttal 1:
Rebuttal: Thank you for your insightful suggestions and feedback. We have responded to the key concerns in the details below.
* Q1: Limited Benchmark Comparisons.
**Response:** While recent state-of-the-art LMMs such as QwenVL2-7B and InternVL2.5-7B achieve higher MMBench scores, it is important to note t... | Summary: The paper proposes an early-fusion method for vision-language reasoning. They claim to have pre-decoder that extracts visual information from raw vision embeddings based on text input and a post-decoder to process fused multi-modal embeddings and generate text responses. The experiments suggest that the method... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and feedback. We address the primary concerns as follows.
* Q1: Clarify more beyond stating that early fusion leads to efficient data usage.
**Response:** We propose utilizing a pre-decoder to fuse image and text data in the early stages of processing. **T... | null | null | null | null | null | null |
SHARP-Distill: A 68× Faster Recommender System with Hypergraph Neural Networks and Language Models | Accept (poster) | Summary: This paper focuses on the teacher-student knowledge distillation. The teacher model use contrastive learning to combine HGNN and a pre-trained LLM, which generate collaborative and semantic features, respectively. The student model is a lightweight GCN. Both response-based and feature-based knowledge distillat... | Rebuttal 1:
Rebuttal: **Essential References Not Discussed**
We thank the reviewer for highlighting recent work on LLM-based recommendation and distillation, including DLLM2Rec [1], SAID [2], and POD [3]. These are valuable contributions to this fast-moving area, and we have now incorporated a discussion of them into ... | Summary: This paper introduces SHARP-Distill, a knowledge distillation framework combining Hypergraph Neural Networks (HGNNs) with language models to improve recommendation quality while reducing inference time. The teacher-student approach uses HGNNs for user-item embeddings and DeBERTa for extracting textual features... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback and are grateful for the recognition of our work’s strengths, including inference speedup, empirical rigor, and the novel integration of hypergraphs with language models.
## Weakness 1) Differentiating RecSys-specific distillation from general gr... | Summary: The paper proposes SHARP-Distill, a framework which uses DeBERTa language model as teacher model to distill HGNN-based recommenders to enhance recommendation performance and inference speed. A contrastive leaning mechanism is leveraged to efficiently inherit the structural and semantic knowledge. Experiments o... | Rebuttal 1:
Rebuttal: ### Claims And Evidence:
We appreciate the reviewer’s thoughtful critique regarding the fairness of our inference-time comparison and broader deployment considerations.
First, we clarify that our comparison is primarily with other **distillation-based models** (e.g., KRD, LightHGNN), making the ... | Summary: The paper introduces SHARP-Distill, a knowledge distillation framework designed to enhance the efficiency of recommender systems while preserving recommendation accuracy. It employs a teacher-student architecture where the teacher model integrates Hypergraph Neural Networks (HGNNs) to capture high-order user-i... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's concerns about preprocessing details and resource analysis. We have added the following information to address these points:
### Weaknesses1 and 2. Teacher Model Training Time and Complexity:
As shown in **Table 1**, our teacher model requires only 4.2 hours to train... | null | null | null | null | null | null |
Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces | Accept (poster) | Summary: The paper proposes a system for automatically generating and optimizing parallel program mappers. Particularly, it tries to do this via using a generative optimization approach aided by an "agent-system interface" which uses a DSL to allow LLMs to write code at a high-level.
Empirical results show that it ca... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive review.
**Q1: How much domain expertise (about the benchmark domain and the LLM capabilities) is needed to expand the ASI and the prompt for the benchmark? More practically, do we need experts who write the mappers to design DSL encoding appropriate p... | Summary: This paper proposes a system powered by large language models (LLMs) to automate both the generation and optimization of mapper code. Specifically, it introduces a Domain-Specific Language (DSL) that provides a high-level interface encapsulating all performance-critical decisions required for mapper generation... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful and constructive feedback.
**Q1: What is your DSL grammar?**
In the revision, we will include a complete description of the DSL syntax in the Appendix, covering its constructs for task placement, memory allocation, layout specification, and index mapping, as shown be... | Summary: The paper introduces an innovative framework aimed at automating the process of optimizing parallel program performance using large language models. The proposed system employs a Domain-Specific Language to simplify the generation of mapping code and uses a mechanism called AutoGuide to turn raw execution feed... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review! We address the concerns below:
**Q1: Performance metrics could be extended**
We appreciate your suggestion and welcome the opportunity to clarify our evaluation methodology and expand the reported statistics.
In our setting, reporting the best result acros... | null | null | null | null | null | null | null | null |
Algorithm Development in Neural Networks: Insights from the Streaming Parity Task | Accept (oral) | Summary: This is an interesting work that studies the development of algorithms in RNN. It combines theory and experiments, which I think is a great plus. The theory and experiments are novel, showing how representations become merged in a linearized dynamical theory
Claims And Evidence: The claims are validated, and ... | Rebuttal 1:
Rebuttal: Thank you for the feedback and spending your time reviewing our paper. Please find below responses to your comments and the changes we will make to the paper.
***Experimental Designs Or Analyses***
- The RNN was initialized at small weights, which is why we see few states at initialization in Fi... | Summary: The authors provide an in-depth analysis for an RNN solving the Streaming Parity Task. Specifically, they extract a computational graph from the network at different training phases. Once this graph becomes cyclic, the network can generalize to longer times. An analytical treatment of how learning dynamics aff... | Rebuttal 1:
Rebuttal: Thank you for the feedback and spending your time reviewing our paper. Please find below responses to your comments and the changes we will make to the paper.
***Claims And Evidence***
The phrasing of the sentence in line 155 is not clear enough, so we will amend it to avoid confusion. What is m... | Summary: This paper explores how neural nets learn automata through training, focusing on a parity task in RNNs, with a short foray into a modular arithmetic task in transformers at the end. They are able to (in a very satisfying way!) theoretically derive equations governing the merging of states in the RNN under some... | Rebuttal 1:
Rebuttal: Thank you for the feedback and spending your time reviewing our paper. Please find below responses to your comments and the changes we will make to the paper.
***Questions For Authors***
- The intuitions and theoretical model surrounding state mergers are independent of the task and not affected... | Summary: This paper studies the learning dynamics in RNNs trained on a toy-task to understand what conditions influence generalization in RNNs. The authors first study RNNs trained on the streaming parity task, and group together RNN hidden states to construct Discrete Finite Automaton (DFA) proxy-models of the RNN. Th... | Rebuttal 1:
Rebuttal: Thank you for the feedback and spending your time reviewing our paper. Please find below responses to your comments and the changes we will make to the paper.
***Weaknesses***
- We are unsure if this is related to the question about equation (16), but for clarity we would like to note that in equ... | null | null | null | null | null | null |
Using Unsupervised Dynamic Feature Selection to Enhance Latent Representations | Reject | Summary: This paper is concerned with feature selection for unsupervised learning. Feature selection is the act of selecting a subset of observed variables to either improve interpretability, reduce overfitting/improve performance, or reduce computation costs. In this work, authors propose a differentiable feature sele... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. To respond to the reviews received, we have added several new results to the paper, as well as revised important parts of the text. Below is a detailed response to your queries:
```
can authors show an ablation across sample size?
```
- We have performed a... | Summary: This paper proposes a Dynamic Data Selection (DDS) method, which is an unsupervised dynamic feature selection method designed to enhance latent representations. The authors claim that DDS can improve the model performance in some unsupervised tasks, such as clustering and world models, by removing noisy or red... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for this kind evaluation. Below we will answer some of the concerns regarding this contribution.
```
The authors claim that DDS is invariant to the maximum number (i.e. M) of selected features, but no ablation studies about M are presented.
```
- We agree with ... | Summary: This paper proposes a method for pixel masking. The masking module is introduced to mask certain pixels of the input image and replaces the original input image with its masked version. The module is trained using a reconstruction loss, and the paper claims that using such masked version of the input during bo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insights. Below we will discuss some of the concerns point by point.
```
why this method contributes to improving the latent representation
```
- The paper now explains the intuition behind our idea. In essence, we argue that every sample often contains information... | null | null | null | null | null | null | null | null |
🏆 COPA: Comparing the Incomparable to Explore the Pareto Front | Reject | Summary: The paper proposes COPA (Comparing the Incomparable to Explore the Pareto Front), a novel approach for comparing and aggregating multiple objectives in machine learning model selection. The authors address the challenge of meaningfully comparing objectives with different scales and semantics (e.g., accuracy vs... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and positive feedback. We are especially grateful for the kind words towards our work, acknowledging its importance and potential impact on modern ML. It is also encouraging to see the reviewer confirming the validity of our approach and derivations, as w... | Summary: This paper proposed "COPA: Comparing the Incomparable to Explore the Pareto Front". The authors claim that it is often unclear
how one should compare, aggregate and, ultimately, trade-off these objectives, as they might be measured in different units or scales. The author proposed to make incomparable objectiv... | Rebuttal 1:
Rebuttal: We thank the reviewer for their work, and we are happy to hear that the cases for which we apply COPA are interesting, and that our evaluation is new and appropriate. We hope the following helps the reviewer better understand our work.
> This paper combine MOO and modern LLMs.
> This paper is hi... | Summary: The goal of the paper is to address the challenge of multi-objective machine learning evaluation where objectives are often incomparable due to differing semantics, units, and scales (e.g., comparing model performance and CO2 emissions). It proposes a novel method, COPA (Cumulative-based Optimization of the Pa... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback. There seems to be a misunderstanding with our work, which we believe to address below.
> As long as we have a perfect Pareto front, practitioners can immediately find their own optimal choice
We politely disagree. As stated in the intro: ***Paret... | null | null | null | null | null | null | null | null |
Extreme Value Policy Optimization for Safe Reinforcement Learning | Accept (poster) | Summary: The paper introduces Extreme Value policy Optimization (EVO), an algorithm that enhances safety in reinforcement learning by leveraging Extreme Value Theory (EVT) to model and exploit extreme reward and cost samples. EVO features an extreme quantile constraint to capture tail risks and an extreme prioritizatio... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer o4Co.
>The integration of EVT and the proposed mechanisms adds complexity to the algorithm. This might make it challenging to implement and tune for ... | Summary: The paper proposes Extreme Value Policy Optimization (EVO), a novel algorithm for safe reinforcement learning (RL) that addresses rare but high-impact extreme events in constrained RL (CRL). Traditional CRL methods optimize expected cumulative costs (e.g., $J_C(\pi) \leq d$), which overlook tail risks (e.g., "... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer hnao.
>Comments regarding the accuracy of GPD fitting and the bias in GPD parameter estimation.
**Response:**
We appreciate the reviewer’s insightful... | Summary: This paper presents the Extreme Value policy Optimization (EVO) algorithm for safe reinforcement learning. It integrates Extreme Value Theory (EVT) to model and utilize extreme samples. EVO introduces an extreme quantile constraint and an extreme prioritization mechanism. Theoretically, it has a lower constrai... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer bFMW.
>In lines 210-211, the conditional probability does not follow the GPD. According to theorem 3.1, it only follows GPD as $q_\mu \to \infty$. The... | Summary: The authors propose a novel approach, Extreme Value Policy Optimization (EVO), to handle rare but high-impact extreme value events by using the Extreme Value Theory (EVT). The EVO introduces an extreme quantile optimization objective and an extreme prioritization mechanism. Extensive experiments are conducted ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's positive and insightful comments. The following are the detailed responses to the points raised by Reviewer csFL.
>The paper depends on the assumption that the extreme value events follow Generalized Pareto Distributions, which may not be true.
**Response:*... | null | null | null | null | null | null |
Great Models Think Alike and this Undermines AI Oversight | Accept (spotlight poster) | Summary: The authors design a metric for measuring model similarity, using a combination of model performance, the type of mistakes as well as probabilistic decisions made by the model. The authors demonstrate that for various pairs of models, there is a high correlation between the measured similarity between the pair... | Rebuttal 1:
Rebuttal: We are glad you found our **metric useful, experiments well-designed, evidence sufficient, and motivation clear**. Given these positive points, we were a bit surprised by the recommendation. We hope the new analyses and clarifications below address the concerns.
**(W1)**
> The tasks that are used... | Summary: The paper proposes a similarity metric between LLMs based on their logits, which measures the similarity of mistakes two models make on a task. The authors use this similarity metric to perform a variety of analyses. In particular they find similarity can predict scores from LLM judges, weak-to-strong generali... | Rebuttal 1:
Rebuttal: We are grateful for your strong support of our work. We are glad you that you found our claims nuanced and well-supported by comprehensive, rigorous experiments.
On your question about how we compute the “elicitation $\cup$ complementary” ceiling estimate in Table 3, we should indeed have made th... | Summary: This paper investigates the challenges of AI oversight when using Language Models (LMs) to evaluate and supervise other LMs. It highlights how model similarity can undermine oversight, as similar models tend to make correlated mistakes and exhibit affinity bias—where they rate outputs from similar models more ... | Rebuttal 1:
Rebuttal: We are glad you liked the motivation and experimental design. We hope our new analyses address your concerns.
**(W1)**
> Comparison with alternative similarity metrics (e.g., KL Divergence, JSD, RSA)
Divergence metrics have desirable information theoretic properties, but **we did not find any wa... | Summary: This paper introduces a probabilistic metric for model similarity that adjusts for chance agreement due to accuracy, distinguishes different types of mistakes, and incorporates confidences. Using this metric, the authors reveal three key insights:
1. LM judges demonstrate affinity bias, favoring models similar... | Rebuttal 1:
Rebuttal: We appreciate that you found our metric well-motivated (with comparisons to alternatives), experiments comprehensive, and observations insightful.
> The proposed similarity metric seems to be tailored for MCQ tasks. It would be insightful if some discussions on possible extensions to other tasks ... | null | null | null | null | null | null |
Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits | Accept (poster) | Summary: This paper is the first to study MAB with interference. The learning model in this paper requires each node to take the same action and assumes that the interference intensity decays with distance. The paper proposes an EXP3-IX algorithm based on exposure mapping, achieving a high-probability regret upper boun... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We appreciate the feedback and would like to share our responses below.
*1. Definition of Regret:*
Alternatively, one could define the benchmark to be the optimal "personalized" treatment assignment from a function class $\mathcal{F}$. In this case, our a... | Summary: The authors study a multi-armed bandit problem where interference (treatment of one arm affects the outcome of others) exists. The authors theoretically prove that a switchback policy achieves optimal regret. They provide a novel method based on clustered randomization and prove that the regret of the proposed... | Rebuttal 1:
Rebuttal: Thanks for your feedback.
*"How does the choice of the learning rate and the IX parameter beta affect the performance of the proposed method?"*
Good question. Intuitively, $\beta$ controls the forced exploration of arms with low score. This aligns very well with Theorem 4.6, where $\beta$ appe... | Summary: The paper presents optimal expected regret bound and presents a high-probability regret bound in presence of correlated rewards among the arms.
post-rebuttal: I wish to thank the authors for the reply. I will keep my score.
Claims And Evidence: Claims are well-supported by evidence.
Methods And Evaluation... | Rebuttal 1:
Rebuttal: Thanks for the feedback.
Q: *"It is not completely clear methodology-wise what is the essential difference between stochastic bandits that consider possibly more general notions of interference and the present paper that considers a specific notion of interference and adversarial reward."*
A: Go... | Summary: The authors combine Auer's EXP3 policy framework, the Horvitz-Thompson IPW (inverse propensity weighting) estimator, along with implicit exploration, and a clustered randomization scheme,
in order to achieve the optimal $O(\sqrt T)$ regret bound ($T$ being the horizon), while admitting a high-probability bound... | Rebuttal 1:
Rebuttal: Thanks for your feedback!
Q: *"In the classic reference 'Logarithmic regret algorithms for online convex optimization' by Hazan et al., it is shown how
regret bounds can be converted into ..."*
A: This is an interesting thought. First, their result is not applicable to this work, since our re... | null | null | null | null | null | null |
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead | Accept (poster) | Summary: This paper propose a method to efficiently serve numerous (thousands) of LoRA adapters for large language models. They propose a joint diagonalization-based compression method that significantly reduces storage and serving overhead while preserving model performance. To effectively scale the LoRA to the advert... | Rebuttal 1:
Rebuttal: Thank you for the review. Please see our responses below.
---
## 1. Shared Basis and Clustering Approach
Our method relies on a shared basis for each cluster. Although this approach implicitly assumes some similarity among LoRA adapters, the clustering strategy is designed to work effectively e... | Summary: This work focuses on a multi-LoRA serving system and significantly enhances throughput. The key approach involves compressing a collection of LoRA adapters to share a common low-rank space. This joint compression effectively reduces the total number of parameters during inference, leading to improved serving e... | Rebuttal 1:
Rebuttal: Thank you for the review. Please see our responses below.
---
## 1. Deciding the Optimal Number of Clusters
Please see Section 6.5 for hyperparameter recommendations. Determining the optimal number of clusters does require some hyperparameter tuning; however, our experiments indicate that the m... | Summary: The paper addresses the challenge of efficiently serving large numbers of LoRA adapters in real-time inference settings. Existing solutions require frequent loading and offloading of LoRAs due to limited GPU memory. The authors introduce a joint compression technique where multiple LoRAs are compressed into a ... | Rebuttal 1:
Rebuttal: Thank you for the review. Please see our responses below.
---
## 1. Throughput Comparison and SOTA s-LoRA
The vLLM multi-LoRA baseline in our experiments already incorporates advanced optimizations such as efficient scheduling and non-blocking CPU-GPU communication when swapping LoRAs, as well ... | Summary: The authors propose a method that efficiently handle the problem of serving thousands of LoRA adapters for LLMs when apply on many tasks by compressing them into a shared basis with LoRA-specific scaling matrices. With number of LoRA become larger, to scale further, they use clustering-based compression, reduc... | Rebuttal 1:
Rebuttal: Thank you for the review. Please see our responses below.
---
## 1. Clustering Basis: Weight Structure vs. Task Similarity
Our approach relies solely on the LoRA weights, meaning that the clustering is driven by the intrinsic weight structure rather than an explicit measure of task similarity. ... | Summary: This paper introduces a novel framework for efficiently managing a large set of LoRA adapters. The authors present a joint diagonalization based (JD) algorithm in both a full and a diagonal variant, which compresses multiple LoRA weights by decomposing them into a shared basis and adapter specific scaling matr... | Rebuttal 1:
Rebuttal: Thank you for the review. Please see our responses below.
---
## 1. LoRA Adapter Diversity and Task Coverage
Please see **Table 3** in the Appendix, which lists all 1000 tasks drawn from *Super-Natural Instructions: Generalization via Declarative Instructions on 1600+ NLP Tasks*. This dataset c... | Summary: This paper considers the problem of serving a large amount of LoRA adapters for the same LLM. This is a very practical scenarios where each LoRA adapter correspond to one specific task. If one naively switches between different adapters, the throughput will degrade a lot when the number of adapters is large. S... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the novelty of our method and its throughput gains over naive solutions.
We emphasize that our joint compression approach effectively addresses the GPU memory constraints and the overhead associated with loading and unloading LoRA adapters and is accompanied ... | null | null |
Generalized additive models via direct optimization of regularized decision stump forests | Accept (poster) | Summary: The authors propose an alternative framework to train GAM models with piecewise constant shape functions based on an alternating optimisation algorithm. A specific regularization strategy is proposed to deal with the issue of overfitting when directly optimizing this type of models.
The authors find that this ... | Rebuttal 1:
Rebuttal: Thank you for providing us with a very valuable and insightful review! We greatly appreciate your effort in reviewing our paper.
* **Convergence to global optimum.** The problem of learning an optimal decision stump forest is computationally intractable in general. Even in the case of 1D splines,... | Summary: This paper proposes an alternating optimization method for training an additive model composed of decision stumps. The optimization procedure alternates between two steps: (1) selecting a feature and determining the optimal split value for a decision stump, and (2) jointly optimizing all coefficients through a... | Rebuttal 1:
Rebuttal: Thanks for providing an insightful and constructive review! We greatly appreciate your effort in reviewing our paper.
* **Claim on novelty.** To be precise, the only place we make a claim of doing something for the first time is with respect to ***learning stump forests with good generalization w... | Summary: The paper introduces a method to fit decision tree stumps per feature, which can be interpreted as a generalized additive model. To mitigate overfitting, the authors propose a smoothness constraint that is optimized jointly with the stump parameters. Unlike approaches such as EBMs that greedily add decision tr... | Rebuttal 1:
Rebuttal: Thank you for providing us with a very valuable and insightful review! We greatly appreciate your effort in reviewing our paper.
* **Modeling discontinuities.** You are absolutely right that NAMs (as well as traditional cubic splines) are limited in their ability to capture sharp discontinuities ... | null | null | null | null | null | null | null | null |
Efficient LiDAR Reflectance Compression via Scanning Serialization | Accept (poster) | Summary: This paper proposes a reflectance compression method based on serialized LiDAR data. Specifically, the method first converts 3D LiDAR point clouds into 1D sequences through scanning order serialization, where each point is labeled with a context representation that includes the sensor scan index, radial distan... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments and constructive suggestions. Thank you for recognizing this paper *provides substantial guidance for future research* with *reasonable writing* and *sufficient experimental evidence*. Below, we provide detailed responses in the hope of addressing y... | Summary: The paper presents SerLiC, a novel serialization-based neural compression framework specifically designed for LiDAR reflectance data. The main contributions and findings of the study include:
1. Serialization of LiDAR Data: SerLiC transforms 3D LiDAR point clouds into 1D sequences through scan-order serializa... | Rebuttal 1:
Rebuttal: **1. Deeper Analysis of SerLiC’s Limitations**
We appreciate your insightful observations and are pleased to provide a comprehensive response below.
1.1 Adaptability to Non-Rotational LiDAR Systems.
Non-rotational LiDAR's scanning also follows a specific order, and SerLiC maintains strong compa... | Summary: This paper proposes an algorithm for LiDAR point compression using LiDAR reflectance and serialization. While many studies only use the point location which is one part of the LiDAR sensor measures, this work focuses on the necessity of the LiDAR reflectance that may involve the surface attribute, which is als... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback. Thank you for recognizing the *highly admirable compression benefit* presented in our study and acknowledging its potential to be *influential to future researchers* and *impactful to this field*. We highly value this opportunity to address your co... | null | null | null | null | null | null | null | null |
In-Context Linear Regression Demystified: Training Dynamics and Mechanistic Interpretability of Multi-Head Softmax Attention | Accept (poster) | Summary: The authors find that a multi-head softmax attention effectively becomes a two-head softmax attention network that approximates linear attention better than a single-head softmax one. The advantage of softmax based attention over linear attention is that one does not have to train separately for different cont... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive feedback and appreciation of our work. Here's our clarifications on theoretical analysis.
**On Informal Theoretical Claims.** We would like to clarify that most of the theoretical arguments presented in this paper are rigorous, with the exception... | Summary: The paper provides an comprehensive understanding of multi-head softmax attention in conducting ICL for linear regression tasks. Through empirical investigation, it observes the specific patterns for the optimal parameters within in the multi-head transformer structures and the superiority of multi-head over s... | Rebuttal 1:
Rebuttal: Thank you for your comments and assessments. Here's our response to the comments.
**On Comparision with Existing Work.**
- **Comparison with [1].**
- (`[1] corresponds to single-head case in our paper`) [1] considers the multi-head attention for **multi-task** linear regression, where the num... | Summary: This paper investigates the training dynamics of multi-head softmax attention in in-context learning. Through experimental analysis, the authors discover two key patterns: (1) QK weight matrices develop a diagonal structure, with diagonal elements being nearly uniform across all heads, and (2) QK weights and e... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and appreciation of our work. Here's our response to the questions.
**On Heuristic Derivation and Two-Head Gradient Flow.**
We acknowledge that in Section 4.2, we employ a heuristic derivation to explain how the observed patterns emerge du... | null | null | null | null | null | null | null | null |
The Expressivity of Fixed-Precision Transformers without Positional Encoding | Reject | Summary: The paper explores the expressive capabilities of transformer decoders constrained by a fixed-precision setting, such as a specific floating-point arithmetic, and with limited positional embedding, utilising formal language theory.
Specifically, the paper posits that if a particular assumption concerning the ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback. We greatly appreciate your careful reading and insightful suggestions, which have helped us clarify key aspects of the paper. Below, we respond point by point to the concerns raised.
# Weaknesses
## 1. Inprecision of ... | Summary: The authors study the expressivity of transformers while considering the practical constraints of real-world usage, such as fixed-point precision. The authors show that without positional encoding, transformers can only represent finite and co-finite languages, which are subclasses of regular language. Adding ... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and thoughtful review.
We are encouraged by the recognition of key strength and we appreciate the thoughtful suggestions for improvement.
We greatly appreciate your recognition of the strengths of our work, as well as your thoughtful suggestions for improvemen... | Summary: This paper investigates the expressivity of the transformer architecture when it is constrained to operate at fixed numerical precision and not involve any infinite parameters -- a setup seemingly matching real-world setups closely. Perhaps surprisingly, the results indicate that the architecture can only reco... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for the constructive and insightful feedback.
# Weaknesses:
We thank the reviewer for pointing out the apparent contradiction between our claim ("fixed-precision Transformers behave as efficient lookup tables") and the observed generalization in experiments. We ack... | Summary: This paper demonstrates that fixed-precision Transformer decoders without positional encoding are limited to recognizing only finite or co-finite languages, and modest expressivity gains are made when adding positional encodings or relaxing parameter constraints. By performing these expressivity analyses in le... | Rebuttal 1:
Rebuttal: We greatly appreciate the valuable comments provided by reviewer Je32.
# Weakness: rigorous of proofs
We agree with the reviewer’s concern regarding the rigor of our proofs.
In the current manuscript, we prioritized clarity of explanations. However, we also acknowledge that the proofs are not suf... | null | null | null | null | null | null |
Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape | Accept (poster) | Summary: The authors present an approach to improving generalization in PEFT. They argue that generalization is strongly correlated with flat minima and that existing empirical approaches are not directly applicable to PEFT. To address this, they propose Flat-LoRA, an extension of SAM that facilitates the discovery of ... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. We address your concerns point by point as follows:
---
**Q1:** Table 4 does not provide variance.
**A1:** Following your suggestion, we rerun the experiments and report the variance as follows:
| Method | MMLU | DROP | BBH| Human-Eval... | Summary: **1. Summary of Contributions**:
The paper introduces **Flat-LoRA**, a novel method for low-rank adaptation (LoRA) that aims to find parameter-efficient fine-tuning solutions residing in a flatter region of the full parameter space. The authors identify that standard LoRA, while efficient, might converge to mi... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. We address your concerns point by point as follows:
---
**Q1:** The performance of Flat-LoRA depends on the choice of the perturbation strength ($\sigma$), and finding the optimal value might require tuning.
**A1:**
We fully understand you... | Summary: This paper proposes Flat-LoRA, a novel approach to improving Low-Rank Adaptation (LoRA) by incorporating a Bayesian expectation loss objective and random weight perturbations to encourage flatter minima in the full parameter space, all while maintaining computational efficiency.
Claims And Evidence: The paper... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. We address your concerns point by point as follows:
---
**Q1:** The method can be viewed as a computationally efficient alternative to SAM rather than an entirely novel approach.
**A1:** SAM has been proven to be an effective training strategy... | Summary: The paper proposes “Flat-LoRA,” a parameter-efficient fine-tuning method that adds random weight perturbations (with an intelligent weight dependent scaling), in order to achieve flatter minima and improve generalization. Experimental results on both vision (CLIP and Stable Diffusion) and language (T5, Llama-2... | Rebuttal 1:
Rebuttal: Thanks for your detailed and constructive comments. We address your concerns point by point as follows:
---
**Q1:** The paper does not explicitly show train-vs-test loss curves or a generalization gap. Hence, it is unclear whether the gains are from genuinely improved generalization rather than l... | null | null | null | null | null | null |
CellFlux: Simulating Cellular Morphology Changes via Flow Matching | Accept (poster) | Summary: This paper proposes CellFlow, a generative model for cell microscopy images in the presence of chemical and/or genetic perturbations. In contrast to existing methods that tackle this problem, CellFlow can explicitly take into account batch effects by learning a distribution-level mapping between unperturbed (c... | Rebuttal 1:
Rebuttal: We thank Reviewer L3Zf for their thoughtful comments and for recognizing the paper’s well-supported claims, appropriate methodology and evaluation, sound theoretical foundation, strong experimental results, and relevance to the cell biology domain. We address their comments below:
---
> **Metric... | Summary: This work uses a flow-based conditional generative model applied to cellular imaging, with the goal of synthetically generate, given a reference control cell and a perturbation (either chemical, genetic, or both), a novel cell image illustrating the effects of the perturbation. Cellular morphology prediction i... | Rebuttal 1:
Rebuttal: We thank Reviewer Yg4R for their appreciation of the paper’s beautiful writing and illustration, proper experimental design and evaluation, and strong relevance to the biology and cell imaging community. We address their comments below:
---
> **Claim 1 (effect of sample size on FID/KID):** *Coul... | Summary: This work introduces CellFlow, an image-generative model designed to simulate cellular morphology changes induced by chemical and genetic perturbations. It leverages flow matching, a generative modeling technique, to learn a distribution-to-distribution map that transforms unperturbed cell states into perturbe... | Rebuttal 1:
Rebuttal: We thank Reviewer Z8BU for recognizing the paper’s proper evaluation criteria, sound theoretical claims, well-designed experiments, comprehensive supplementary material, as well as its innovative approach, strong performance, and biological relevance. We address their concerns below:
---
> **Tec... | null | null | null | null | null | null | null | null |
Learning without Isolation: Pathway Protection for Continual Learning | Accept (poster) | Summary: The paper introduces a novel continual learning method specifically designed to address the problem of catastrophic forgetting, which is a significant challenge in the field of machine learning when models are required to learn from a sequence of tasks. The proposed approach leverages pathway protection techni... | Rebuttal 1:
Rebuttal: **Response to Claims:** Thanks for pointing out the issue. We would like to explain it as follows.
1. In continual learning, considering the lack of correspondence between neurons in Model 1 and Model 2, it is possible that the function of the p-th neuron in Model 1 is very similar to that of the... | Summary: The paper proposes a novel continual learning framework that assigns distinct neural pathways to different tasks, enabling knowledge retention while replacing traditional masking & pruning methods. The authors use graph matching for model fusion, leveraging neural network properties by maximizing similarity al... | Rebuttal 1:
Rebuttal: ****Response to C1:**** Thank you for the reviewer's reminder. We sincerely apologize for the misunderstanding caused by our negligence. A precise description will be provided in future revisions. The concept of "Activation Level" refers to the average magnitude of the weights obtained after activ... | Summary: This paper proposes a new framework for continual learning (CL) called Learning without Isolation (LwI), which introduces pathway protection as a mechanism to mitigate catastrophic forgetting. Unlike traditional CL methods that focus on parameter protection, LwI prioritizes preserving activation pathways in de... | Rebuttal 1:
Rebuttal: **Response to C1:** Thank you for the reviewer's reminder.
- We conducted a corresponding analysis, including the optimization of our proposed method in terms of time complexity. In the context of our hierarchical matching approach, we analyze its time complexity as follows. Given a deep network w... | Summary: The paper introduces a novel approach to continual learning, termed "Learning without Isolation" (LwI), which aims to mitigate catastrophic forgetting by protecting distinct activation pathways for different tasks within a deep network. The key idea is to allocate unique pathways for each task, ensuring that k... | Rebuttal 1:
Rebuttal: **Response to W1:** Thank you for the reviewer's reminder. We have supplemented our theoretical derivations.
## 1. Core Theoretical Framework
### 1.1 Shallow Layers (Shared knowledge)
**Core:** Minimize weight differences through optimal transport alignment of similar channels
1. Parameter Update
... | null | null | null | null | null | null |
Leveraging Randomness in Model and Data Partitioning for Privacy Amplification | Accept (poster) | Summary: The submission analyzes privacy amplification for Renyi DP in two settings where $N$ (sets of) records are independently assigned to $k$ out of $d$ components of DP-SGD:
(1) Data partitioning, where each record from a dataset of size $N$ contributes to $k$ out of $d$ gradient steps ("balanced iteration subsamp... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review of our paper and their valuable feedback.
Our paper is indeed the first to point out and quantify the privacy gain from model parallelism techniques already employed in federated learning.
Following the reviewer's suggestions, we have done more exp... | Summary: The paper shows how Renyi DP guarantees can be amplified under two different kinds of data sub-sampling and partitioning strategies. The first is where all data points are used for the same number of iterations but in randomly distributed steps. The second is where different parts of the model are updated with... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and their valuable feedback. We will incorporate their comments in our revised paper.
Following the reviewer's suggestions, we have done additional experiments for both centralized and federated settings. In the centralized setting, training ResNet101 using... | Summary: The paper explores how inherent randomness in machine learning training can be used for privacy amplification, specifically model partitioning and data subsampling. These methods can potentially enhance the training privacy without adding excessive noise.
Claims And Evidence: It is somewhat unclear whether th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and valuable feedback.
We would like to clarify their main question about "whether the proposed
stronger privacy comes at the cost of worse utility or convergence rate compared
to the canonical DP-SGD method." The main contribution of our paper is to develo... | Summary: The paper proposes a unified privacy analysis for the applications of model and data partitions. The crucial theorem as shown in Theorem 3.1, which is novel and non-trivial to my best knowledge, states the renyi-divergence between a Gaussian distribution and a mixture of Gaussians. Upon to this theorem, the pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and their valuable feedback.
We would like to clarify their main concern regarding "a straightforward comparison between the proposed analysis and the analysis in the literature" for the model splitting methods in Section 3.2. We note that our paper is the ... | null | null | null | null | null | null |
Near Optimal Best Arm Identification for Clustered Bandits | Accept (poster) | Summary: This paper introduces a Best Arm Identification problem with clustering structures. Specifically, given $N$ agents and $M$ bandits instances (Usually $N>M$), each agent faces one of the bandit instance, but the mapping is unknown. The goal is to identify the best arm for each agent with probability at least $1... | Rebuttal 1:
Rebuttal: **Knowledge of $\eta$:** We run additional experiments to validate the robustness of our schemes. Please refer to our rebuttal of Reviewer UFPL.
**Different bandit instances have Different best arms:** Our objective is to find the best arm for every agent, and so it seems reasonable to *define* t... | Summary: The paper explores the problem of identifying the best arms in a multi-agent bandit setting, where agents form (unknown) cluster-based structures.
To address this challenge, the authors propose two algorithms. The first algorithm, CI-BAI, first clusters the agents and then identifies the best arm for a randoml... | Rebuttal 1:
Rebuttal: **Current state-of-the-art algorithm for Best Arm Identification (BAI):** We choose Successive Elimination (SE) over other state of the art algorithms like Track And Stop (TAS) or Lower Upper Confidence bound (LUCB) for a number of reasons:
(i) We aim to address the problem of Clustering and BAI... | Summary: This work studies the problem of best arm identification for clustering of multi-armed bandits, where $N$ agents are grouped into $M$ clusters, with each cluster solving a stochastic bandit problem. The goal is to identify the best arm for each agent under a $\delta$-probably correct ($\delta$-PC) framework, w... | Rebuttal 1:
Rebuttal: **Additional references** We thank the reviewer for these pointers. As suggested by the reviewer, we will aim to do a more extensive review of the related literature on clustering in bandits. We would like to point out that one key difference between most of the literature there (including the pap... | Summary: The paper considers the problem of federated fixed-confidence best arm identification, where the agents are assumed to be clustered and the agents of the same cluster share the same bandit instance. The authors propose two algorithms, Cl-BAI (cluster-then-BAI) and BAI-Cl (BAI-then-cluster) and show their sampl... | Rebuttal 1:
Rebuttal: **Nearest neighbor and pairwise distance:** The claim being discussed here considers the `bad' event that two agents belonging to the same cluster get assigned to different clusters, i.e., there exists an arm in the union of the active sets of these two agents, whose estimated means for agents $i$... | null | null | null | null | null | null |
Design Considerations in Offline Preference-based RL | Accept (poster) | Summary: The paper provides a theoretical study of offline learning methods from human preferences. The authors first establish a unified framework and relevant assumptions which fit most all of preference-based learning losses. Then, they propose a policy benchmark which is used to measure the quality of a policy outp... | Rebuttal 1:
Rebuttal: We thank you for your encouraging feedback on our work. We address your key questions below.
**1. Relaxing Assumption 3.1**: You are correct in noting that having an exponential dependence on the size of log-ratios is not ideal. But this limitation is shared by a line of prior works [1], [2], [3]... | Summary: This paper explores the theoretical aspects of offline preference-based reinforcement learning (PBRL). It examines a broad range of offline PBRL methods, including DPO and IPO, and establishes theoretical bounds on the sub-optimality of the policies learned by these methods. The analysis is based on specific a... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback on our work. You are right in noting that the main contributions of our work are theoretical, and not in proposing new methods. We address your main concerns with our theoretical results below.
**Squared loss and realizability**:. You are correct that it ... | Summary: The paper offers a theoretical analysis of offline preference-based RL algorithms such as DPO. Specifically, the authors investigate an empirical observation: offline preference-based RL is often worse than online RL, and faces some degeneracies during optimization.
To set up the problem, the authors first se... | Rebuttal 1:
Rebuttal: We thank you for your encouraging and thoughtful comments.
**Severity of restrictions due to Assumption 3.5**: You are correct in noting that Assumption 3.5 places some restrictions on the loss function, but we believe that these restrictions are relatively mild and standard as we discuss below:... | Summary: The paper investigates offline reinforcement learning methods that use a fixed dataset of responses and human preference feedback to align language models. It analyzes how various design choices in methods like DPO, IPO, and SLiC affect the quality of the learned policy. The study provides a unified theoretica... | Rebuttal 1:
Rebuttal: Thank you for the encouraging feedback. We will add the suggested references [1, 2] to our discussion. | null | null | null | null | null | null |
Improving LLM Video Understanding with 16 Frames Per Second | Accept (poster) | Summary: The paper explores high FPS in video understanding with MLLM. It is an interesting and meaningful attempt and the authors employ some techniques to solve the problem of excessive number of tokens. The performance gain is promising in some specialized scenarios like sports as expected.
## update after rebuttal... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful and constructive feedback on our paper. Below, we will respond to the questions you have raised.
---
1: **The evaluation on existing general video benchmarks cannot showcase the advantage of high frame-rate training. & Supplement more scenarios beyond spor... | Summary: This paper studies the problem of high-frame-rate video understanding. The authors claimed that existing methods for video understanding merely sample video frames at a low FPS (mostly lower than 2), where there exists critical information loss. To tackle this problem, they introduce F-16, a novel multimodal l... | Rebuttal 1:
Rebuttal: Thank you for the positive rating for the paper. We sincerely appreciate your recognition of our work.
---
Rebuttal Comment 1.1:
Comment: Thanks for the response from the authors. I'm keeping my original rating. | Summary: This paper proposes a new method F-16 that increases the frame rate of existing video LLM to 16 frames per second (FPS). The paper argues that existing video LLMs, which typically operate on low frame rates (e.g., 1 FPS), lose crucial dynamic visual information. F-16 aims to address this by processing videos a... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful and constructive feedback on our paper. Below, we will respond to the questions you have raised.
---
**1: Can you provide more detailed information about the training procedure, including hyperparameter settings, training time, and the resources (e.g., nu... | null | null | null | null | null | null | null | null |
A Peer-review Look on Multi-modal Clustering: An Information Bottleneck Realization Method | Accept (poster) | Summary: For the mentioned three limitations faced by most current weighted multimodal clustering methods, this paper, inspired by the peer-review mechanism in academia, iteratively considers one modality as the "author" and the remaining modalities as "reviewers" to obtain the peer-review score for each modality. To i... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**1. There are many trustworthy multi-modal classification or clustering methods published recent years. This paper also mentione... | Summary: In this paper, the authors propose a new multi-modal clustering method by information bottleneck method with an interesting peer-review look. This method work in a weighted mechanism with two learning scores, including peer-review and trustworthy score. It is noted that the weight learning process is conducted... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**1. The peer-review look on multi-modal clustering is interesting, have the authors considered using this idea for other problem... | Summary: Most existing methods in multimodal clustering have three core challenges on the trustworthiness, weight learning and parameter learning. Motivated by the peer-review mechanism, this paper solves the multimodal clustering problem and realizes mutual review of different modalities by rotating the roles of "auth... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**1. Lack of discussion on existing trustworthy multimodal clustering approaches. Are there prior weighted multimodal clustering... | Summary: This paper proposes a new peer-review trustworthy information bottleneck method. It designs a multimodal peer review process, in which the modality will iteratively act as an "author" or "reviewer" to conduct peer review to explore the potential relationship, which is quantified as the peer-review score; and t... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and constructive suggestions. We have carefully revised the whole manuscript and provided detailed responses to each point below.
**1. Generally, in a peer-review process, an EiC is also involved. I am interesting about which part stands for the EiC role in t... | null | null | null | null | null | null |
Disturbance-based Discretization, Differentiable IDS Channel, and an IDS-Correcting Code for DNA Storage | Reject | Summary: The authors propose THEA-Code, a IDS-correcting code for storing DNA, where the codes are subject tor insertion, deletion, and substitution errors. Their approach has two main components: first, they train a differentiable model to simulate the IDS channel. Using the trained channel, they additionally train an... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for their valuable efforts. We will revise the manuscript accordingly. We hope our rebuttal has addressed the concerns.**
**Q1**: Is a more realistic setting than MemSim available?
**A1**: Firstly, a simulated channel is essential for training such a model, as... | Summary: They proposed a universal method for designing tailored IDS-correcting codes across varying channel settings.
1. They propsed a disturbance-based discretizationto discretize the features of the autoencoder, which applies a Gumbel SoftMax to code the the alphabet {A, T, G, C}.
2. A simulated differentiable IDS... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for their valuable comments. We hope our rebuttal has adequately addressed the concerns. Minor concerns not mentioned will also be revised.**
**Q1**: The code is empty.
**A1**: This appears to be a cache issue with the anonymous hosting platform, as several simi... | Summary: This work proposes THEA-code, an auto encoder for learning IDS-correcting codes. It does this in two stages: (1) learning a differentiable IDS channel from a ATGC sequences from CIDS, and then (2) using the learned IDS channel to train an auto encoder to automatically learn a IDS-correcting code.
Claims And E... | Rebuttal 1:
Rebuttal: **We sincerely thank the insightful feedback, which is invaluable in improving our manuscript. As this is the only negative score, we genuinely hope our rebuttal has addressed all the concerns and that the reviewer may reconsider the score.**
**Q1**: The code is empty.
**A1**:
This appears to be... | Summary: This paper presents THEA-code, an end-to-end autoencoder-based model for an IDS-correcting code. Extensive experiments demonstrate that THEA-Code can adapt effectively to various IDS channel conditions and outperforms existing IDS-correcting codes on simulated and realistic DNA storage channels. THEA-Code espe... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for their valuable efforts. We will revise the manuscript accordingly. We hope our rebuttal has addressed the concerns.**
**Q1**: The theorem is sometimes hard to follow.
**A1**:
We will revise the main text of Theorem 3.1 for clarity.
Additionally, we will p... | null | null | null | null | null | null |
Optimal Survey Design for Private Mean Estimation | Accept (poster) | Summary: This paper studies how to estimate a population mean from surveys collected from different groups of people. Differential privacy is required at the level of each group At a high level, the mechanism randomly samples users from each group, who then send in their responses plus noise. The population mean is the... | Rebuttal 1:
Rebuttal: Thank you for recognizing that our work contributes to the growing trend of incorporating differential privacy into fundamental statistical methodologies, particularly in stratified sampling.
- Regarding the need for prior knowledge of variances, we agree that this is a limitation. However, in pr... | Summary: The authors propose a DP stratified sampling scheme that can be optimized for various objectives such as population mean estimation or an A-optimal design. The main contributions are a general algorithm to solve the mixed-integer programming problem this creates, as well as closed-form solutions for important ... | Rebuttal 1:
Rebuttal: We are thankful that you acknowledged that we identified a gap in the DP literature regarding survey sampling and provided a successful solution along with a thoughtful experimental evaluation. We hope that our responses below will address your comments.
- **(Experimental Design Comment)** Thank ... | Summary: This submission is about designing stratified sampling schemes for surveys conducted with differential privacy. In stratified sampling, groups may be surveyed at different rates and these per-group estimates then combined. This is a ubiquitous survey method.
The survey setting is an important one for statisti... | Rebuttal 1:
Rebuttal: Thank you for recognizing the contributions of our work. While we acknowledge that this research does not fully resolve all related problems, we are pleased that you see it as a significant step forward. Please let us know if you have any further questions.
---
Rebuttal Comment 1.1:
Comment: I h... | Summary: This paper develops a stratified sampling scheme that minimizes variance while ensuring differential privacy (DP) under the Laplace, Discrete Laplace, and Truncated-Uniform-Laplace mechanisms. The key insight is that stratified sampling can amplify privacy guarantees, but optimal allocation of samples across s... | Rebuttal 1:
Rebuttal: Thank you for your questions. We are happy to address them below:
- The key aspect of our setup that enables an efficient solution is the strong convexity property. When generalizing to other settings, it is important to verify that the resulting objective is still strongly convex, which may need... | null | null | null | null | null | null |
MAS-GPT: Training LLMs to Build LLM-based Multi-Agent Systems | Accept (poster) | Summary: This paper introduces MAS-GPT, a novel multi-agent system generation framework. Specifically, MAS-GPT employs a multi-agent generator model trained using modules such as MAS filtering, inter-consistency assurance, and intra-consistency enhancement. Designed to adapt to the nuances of diverse tasks, MAS-GPT aim... | Rebuttal 1:
Rebuttal: Thanks for your time devoted to reviewing our paper. We are glad to see that you acknowledge that our method is novel, experimental designs are sound, and our paper is insightful. It is encouraging. We would like to address your remaining concerns in the following.
---
**Experimental Des... | Summary: This paper proposes to train an LLM to build multi-agent system. The paper reframes MAS construction as a python coding task and represents MAS as executable python code. One key contribution is a consistency-oriented data construction pipeline that generates high-quality query-MAS pairs. Extensive experiments... | Rebuttal 1:
Rebuttal: We are glad that you acknowledge that our idea is novel, method is comprehensive, and direction is aligned with a broader trend toward developing more streamlined, scalable, and adaptive architectures. Let's address your remaining concerns!
---
**W1:** Representing MAS as python code neglects i... | Summary: The paper introduces MAS-GPT, a novel approach that trains LLMs to automatically generate query-specific multi-agent systems (MAS) in a single inference step. Unlike previous methods requiring manual configuration or multiple LLM inferences, MAS-GPT simplifies MAS creation by reframing it as a generative langu... | Rebuttal 1:
Rebuttal: We are glad to see that you agree with our motivation and that our approach is novel. We notice that your main concern is on the experimental setups, of which we believe is caused by some confusion. (Since the comments in `Claims` are overlapped with `Weaknesses`, so we focus on the latter)
---
... | Summary: The paper presents MAS-GPT, a novel approach that automates the creation of multi-agent systems specifically tailored to user queries using a single inference. The authors address key limitations in existing MAS approaches, namely high manual crafting effort and high computational costs, and propose to simplif... | Rebuttal 1:
Rebuttal: We are glad to see that you acknowledge that our approach is novel, methodologically sound, flexible, and scalable. We are sorry that we leave you some concerns and let's clarify.
---
**W1&Q2:** The generalization capability of MAS-GPT across significantly different or novel task domains remains... | Summary: In this paper, the authors propose to train a GPT to generate the code that represents a Multiagent system, and thus provide a team build for each query. Specifically, the author proposes to use some existing datasets as training samples and run these samples on 40 different predefined systems to form training... | Rebuttal 1:
Rebuttal: Thanks for your appreciation of our idea and motivation. We would like to address your remaining concerns in the following.
---
**Methods:** The motivation for choosing these 40 MAS pools is unclear.
**A:** Sorry for the confusion. Let's clarify.
(1) The key motivation for choosing the... | null | null | null | null |
LLM Data Selection and Utilization via Dynamic Bi-level Optimization | Accept (poster) | Summary: The paper proposes a dynamic bi-level optimization framework to improve data selection and utilization during LLM training. The bi-level optimization includes updating model parameters using data weighted by a weighting model, and optimizing the weighting model based on the model’s updated performance. Experim... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. Below, we address your questions and concerns in detail.
---
**Q1. The Computing Cost of the Bi-Level Optimization**
**A1.** DWM does introduce additional computational overhead. We provide an analysis of this issue in our response to **R3Q... | Summary: The paper introduces a Data Weighting Model (DWM) that dynamically adjusts data weights during LLM training using a bi-level optimization framework. DWM captures evolving data preferences by iteratively updating a weighting model based on validation performance. Experiments on 370M and 1.3B models demonstrate ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and encouraging review. Below, we address your questions and concerns in detail.
---
**Q1. The Training Cost of the Bi-Level Optimization**
**A1.** We would like to clarify that in DWM, we employ a bi-level optimization strategy on the 370M model to separately tr... | Summary: This paper introduces a novel Data Weighting Model (DWM) to enhance data utilization during large language model (LLM) pre-training. DWM provides a dynamic data selection method by dynamically adjusting the weights of data samples within each training batch using a bi-level optimization framework. This framewo... | Rebuttal 1:
Rebuttal: Review 2 Rebuttal
---
Thank you for your thoughtful and detailed review. Below, we address your questions and concerns in detail.
---
**Q1. The Missing Literature**
**A1.** Thanks for your suggestions, and we have added the discussion of these related work below as well as in revision.
Exis... | Summary: This work proposes DWM, to address the limitations of existing data selection methods that ignore dynamic model training dynamics during LLM pre-training. Based on a bi-level optimization, DWM adaptively sets the weights of each data sample in a batch. In experiments, as a plug-and-play module, DWM improves th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and valuable review. Below, we address your questions and concerns in detail.
---
**Q1. Training Cost of DWM**
**A1.** DWM does introduce additional computational overhead. We provide an analysis of this issue in our response to **R3Q1** and will include the discu... | null | null | null | null | null | null |
Constrained Pareto Set Identification with Bandit Feedback | Accept (poster) | Summary: This paper studies the fixed-confidence setting of the Pareto Set under linear feasibility constraints in a multi-objective bandit setting. The authors propose an algorithm and establish its near-optimal theoretical guarantees through information-theoretic lower bounds in the worst case, and validate their app... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and detailed evaluation of our work. Below, we address each point raised by the reviewer.
- Weaknesses
1. Linear constraints are widely used in applications like dose-finding trials, where balancing efficacy and toxicity is crucial (Mark C et al.... | Summary: This paper studies the constrained Pareto set identification (cPSI) problem (with explainability). More specifically, the authors focus on the $(\epsilon, \delta)$-PAC learning setting, and the objective of the agent is to identify a partition of the arm set into three sets (the Pareto set, a set of suboptimal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and for taking the time to evaluate our work. Below, we address each of the points raised in the review.
* **Optimal algorithm for cPSI**
We present an asymptotically optimal algorithm for cPSI in Appendix E. Since we believed that e-cPSI was better sui... | Summary: This work studies bandit pareto set identification with constraints. In particular, the task is to choose arms to pull in each round until the set of feasible and pareto arms is identified with probability at least $1 - \delta$. They give an algorithm that addresses this problem
## update after rebuttal
In t... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and detailed evaluation of our work. Below, we specifically address each point raised in the review.
1. $α$ is a parameter of the algorithm. We introduced generic confidence bonuses in l.255-l.257, and Thm 4.3 upper-bounds the sample complexity o... | null | null | null | null | null | null | null | null |
PDE-Transformer: Efficient and Versatile Transformers for Physics Simulations | Accept (poster) | Summary: This paper presents an enhanced diffusion transformer architecture through the integration of several established techniques, including multi-scale modeling and shifted window attention mechanisms. The improved model is subsequently applied to partial differential equation (PDE) solving tasks. Extensive experi... | Rebuttal 1:
Rebuttal: Thank you for the review and feedback. We want to address your remaining concerns in the following:
**Stronger Baselines** While there can certainly be more baselines, we politely disagree that the baselines chosen are "relatively weak". For example, we compare extensively against scalable operat... | Summary: The paper presents a transformer model called PDE Transformer designed to solve PDE (partial differential equations), therefore potentially allowing for physical simulations. The model is based on the diffusion transformer architecture (DiT), and as such, can be be trained not only for forecasting, but also fo... | Rebuttal 1:
Rebuttal: Thank you for the positive review and feedback.
**Pretraining on Downstream Tasks** Baseline models are not pretrained. We have a version of our own model that is trained from scratch and one that is pretrained. In all cases, our model trained from scratch is better than the baselines and perfor... | Summary: The paper presents PDE-transformer, a new transformer approach trained simultaneously on different physical systems. They propose an alternative to the DiT architecture that is suited for PDEs. Specifically, they use a multi-scale architecture and a shifted-window attention to prevent from the quadratic comple... | Rebuttal 1:
Rebuttal: Thank you for the positive review and feedback.
**MSE vs. Diffusion** This is a very important and interesting issue. We believe that scientific machine learning models should in general be capable of learning the full posterior; however how useful this is still depends on the specific task and ... | Summary: This paper introduce PDE-Transformer, a transformer made for PDE data being able to be incorporated in a supervised learning task or in a diffusion model. The model is extensively tests on various benchmarks.
Claims And Evidence: Yes, the claims seem supported by convincing evidence
Methods And Evaluation Cr... | Rebuttal 1:
Rebuttal: Thank you for the positive review and feedback.
**BF16 mixed precision** That's a good point. With BF16 mixed precision we lose precision compared to FP32, but can train a lot faster. In our experiments, we did not see a difference in the evaluation metrics and training loss when switching betwe... | null | null | null | null | null | null |
DyCodeEval: Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination | Accept (poster) | Summary: This paper proposes DyCodeEval: a dynamic benchmarking approach designed to evaluate the reasoning capabilities of Large Language Models on code tasks under potential data contamination. By starting with a seed programming problem, DyCodeEval leverages multiple agents to extract and modify problem contexts—wit... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments.
> **How Our Dynamic Metric Mitigates Data Contamination**
For the static metric **pass@k**, the same fixed problem prompt is fed to the LLM multiple times, leveraging its sampling capability to generate different outputs. However, since this prompt is publicly ... | Summary: This paper proposes a framework for augmenting existing coding model evaluation datasets by coming up with new scenarios and contexts to generate semantically similar evaluations. The authors use several LLM steps to produce these questions, and evaluate models while attempting to simulate data contamination. ... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and valuable comments
>**Semantically Equivalent Validation and Human Study**
By “semantically equivalent variations,” we mean that the generated problems can be solved by the same code solution as the original. We validate this through:
1. **Automated Validat... | Summary: The paper presents a method for modifying existing LLM coding benchmarks through a 4-stage pipeline to produce new versions of the benchmark that are unlikely to have appeared in training data. This addresses the challenge that LLM developers face when collecting training data and evaluating their models: that... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and valuable comments
---
> **Empirical values of our theorical bounds**
To empirically evaluate the collision rate of our method, we conduct an experiment on HumanEval. First, we run DyCodeEval on HumanEval to generate an initial set of transformed programming pr... | Summary: This paper introduces a novel code LLM benchmark that leverages metamorphic testing to address challenges associated with current benchmarks' reliance on publicly available, human-curated datasets.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: None
Experimental Designs O... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper and valuable comments.
> **Concern about the benchmark's discriminative power**
We appreciate the reviewer’s feedback and the opportunity to clarify our findings. However, we believe there may be some misunderstandings regarding Figure 5.
First, the lower number... | null | null | null | null | null | null |
NextCoder: Robust Adaptation of Code LMs to Diverse Code Edits | Accept (poster) | Summary: This paper addresses two issues: (1) enhancing code language models on code-editing tasks; and (2) mitigating catastrophic forgetting caused by task-specific fine-tuning. To address (1), it proposes a method for synthesizing high-quality code-editing data; to address (2), it introduces Selective Knowledge Tran... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback on our work. We answer the questions asked by the reviewer below:
> My concern lies in why the LLM-generated dataset is realistic and representative? Specifically, ..., how could the synthesized dataset to represent such distribution.
Our synthet... | Summary: The authors present a comprehensive approach to enhance the code editing capabilities of language models while maintaining their pre-existing abilities. Their work addresses two fundamental challenges in this domain: the scarcity of high-quality fine-tuning data for code editing tasks and the phenomenon of cat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback on our work. We answer the questions asked by the reviewer below:
> Authors have not done any analysis on generating data overlap with benchmark ...
The benchmarks considered in the paper are based on manually-created coding problems and solution... | Summary: This paper proposes an approach to handling diverse code-editing requirements. First, it introduces a synthetic data generation pipeline that begins with seed code samples and applies various editing criteria to produce high-quality training data. This pipeline generates pairs of original and modified code alo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback on our work. We answer the questions asked by the reviewer below:
> The scarcity of high-quality fine-tuning data and the risk of catastrophic forgetting during fine-tuning are well-known problems in ML models in general. This raises concerns that... | Summary: The paper introduces an adaptation method for code language models on code-edit tasks. The authors presents a synthetic data generation pipeline that creates code samples paired with edited versions and natural language instructions. The paper states that during fine-tuning, their SeleKT can update the model’s... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback on our work. We answer the specific questions below:
> Reason for selecting some specific hyperparameters for seleKT.
Our preliminary experiments indicated that a sparsity value of 5% and periodicity of 1 epoch were effective across model familie... | null | null | null | null | null | null |
Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants | Accept (poster) | Summary: This paper propose causal effect identification method for proxy variable setup and underspecified instrumental variable setup based on high-order cumulants. In the proxy variable setup, both multiple latent confounders and a causal edge from the proxy variable to the treatment are allowed while only one proxy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and helpful suggestions.
- *There is no related work that is essential to understanding the (context for) key contributions of the paper but are not currently cited/discussed in the paper. However, there are some recent works [1,2] that also use h... | Summary: The paper studies the problem of estimating causal effects in lvLiNGAM via higher-order cumulants. Specifically, the authors consider two setups where a single proxy variable exists and a instrumental variable (IV) exists with multiple treatments. In both settings, the authors provide the effect identification... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and helpful suggestions.
- *Lack of intuitive example or explanations for the main theorems. It could be better to
move the proofs in the main text into the appendix and add more discussion.*
We will use the additional page in the final version o... | Summary: This paper explores causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two challenging scenarios involving latent confounding: (1) a single proxy variable that may influence the treatment and (2) underspecified instrumental var... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed comments and helpful suggestions.
- *While the findings support the effectiveness of the proposed methods, applying them to a real-world dataset may further strengthen the validation and highlight their practical applicability.*
Following the input of th... | null | null | null | null | null | null | null | null |
Federated Node-Level Clustering Network with Cross-Subgraph Link Mending | Accept (poster) | Summary: In this study, the authors investigate two unexplored issues in federated graph learning (FGL), namely: 1) the heavy reliance on labeled graph samples that are difficult to obtain in real-world applications; and 2) the inevitable missing links caused by partitioning a complete graph into several subgraphs. To ... | Rebuttal 1:
Rebuttal: # Response to Reviewer:
**(W1) :**
**Reasons for the low NMI and ARI:** Thanks. The Questions dataset exhibits a significant class imbalance, with 47461 samples for class 0 and 1460 for class 1, which could result in lower NMI and ARI performance. Although class imbalance presents significant di... | Summary: This paper designs a federated graph learning framework called Federated Node-Level Clustering Network (FedNCN) that mends the cross-subgraph missing links to enhance the clustering performance of each client in an unlabeled circumstance while not sharing private data. The work also conducts experiments on sev... | Rebuttal 1:
Rebuttal: # Response to Reviewer:
**1. Clustering centers:** Thanks. In FedNCN, both the client and the server have models that are used to learn the graph embeddings. The method for calculating the cluster centers is the same for both, initialized by K-means and updated iteratively by the model. However, ... | Summary: The authors propose a Federated Node-Level Clustering Network (FedNCN), which is the first attempt to tackle the issue of link missing caused by graph partition in an unsupervised learning scenario. The core idea of FedNCN is to mend the destroyed links using prior clustering knowledge. Extensive experiments h... | Rebuttal 1:
Rebuttal: # Response to Reviewer:
**1. Relationships between three components:** Thanks. In our approach, the local model learning strategy collects and preserves more reliable clustering signals to prepare for the recovery of damaged sample connections. The cross-subgraph link mending strategy utilizes th... | Summary: This paper introduces federated node-level clustering that achieves cross-subgraph link mending under unsupervised circumstances. The proposed approach is mainly composed of three components, i.e., the local model learning scheme that collects and preserves trustworthy clustering signals for destroyed sample l... | Rebuttal 1:
Rebuttal: # Response to Reviewer:
**i. Some concerns:**
**a) Clustering ground truth:** Thanks for your question. The clustering ground truth is the true cluster category of samples. In our paper, it corresponds to the node label. The ground truth is used only in the evaluation stage of this task.
**b) P... | null | null | null | null | null | null |
On the Statistical Mechanisms of Distributional Compositional Generalization | Accept (poster) | Summary: The paper investigates the statistical mechanisms underlying Distributional Compositional Generalization (DCG), focusing on two key questions: 1) whether methods for one DCG task generalize to others, and 2) what statistical properties determine a learning algorithm’s compositional ability. The authors propose... | Rebuttal 1:
Rebuttal: > W1: lack empirical validation.
We present new experimental results to validate the theoretical derivations (**see the "Experiments" section in the Rebuttal of Reviewer XpR4 for details**). Below is a summary of our findings:
1. We confirm that non-trade-off improvements are strongly correlate... | Summary: In this paper, the authors introduce a statistical framework to address two important research questions that have not been explored in prior work. Specifically, they examine whether a method designed for one DCG problem can be applied to another and identify the statistical properties that indicate a learning... | Rebuttal 1:
Rebuttal: ## More background and examples
### Background:
We will include a new section in the Appendix to provide additional background knowledge about **statistical machine learning**. The structure is:
1) Key concepts, including data space, learning algorithms, function space, and the i.i.d. (independe... | Summary: The authors analyze the problem of Distributional Compositional Generalization (DCG). Compositionality in this sense is the ability to model different features in the dataset and the statistical dependency between them. They try to provide statistical tools to assess whether it is possible to transfer one DC... | Rebuttal 1:
Rebuttal: > W1: There is no example, no illustration, not even a toy problem
Actually, we provide the Example 3.2, Example 3.3 and an illustration in Appendix (Page 17).
We have added more examples and background (detail **See ''More background and Examples'' of the rebuttal to Reviewer nkeW**) and expe... | Summary: This paper proposes a theoretical statistical framework for analyzing Distributional Compositional Generalization (DCG). An invariant measure is proposed to evaluate the generalizability of methods across DCG tasks and derive a generalization bound separating the effects of insufficient data from knowledge com... | Rebuttal 1:
Rebuttal: ## 1. Experiments
### 1.1. Experiment Design:
**1. Components and Compositional rule**： We construct two words set A,B satisfying |A|=|B|=1000 and their corresponding element $a_1,a_2\subset A$ and $b_1,b_2\subset B$. $a_1,a_2$ is a partition of $A$ and the same as $b_1,b_2$. $|a_1 |=|a_2|=|b_1... | null | null | null | null | null | null |
The Empirical Mean is Minimax Optimal for Local Glivenko-Cantelli | Accept (poster) | Summary: Background: In this work the authors investigate the question of estimating densities on $\lbrace 0,1\rbrace^\mathbb{N}$ where it is assumed that each index is independent and a sample has the form of a sequence of iid $Bern(p_i)$ random variables, so the density has the form $\mu = \prod_{i \in \mathbb{N}} \m... | Rebuttal 1:
Rebuttal: Thank you for your thorough evaluation. We address your concerns below:
1. Relevance and Venue Suitability
We recognize that our work is theoretical. However, uniform convergence in the Local Glivenko-Cantelli sense is fundamental to learning theory, informing both risk bounds and distributi... | Summary: In the local Glivenko-Cantelli setting, one seeks to learn an unknown distribution $\mu$ over $\{0,1\}^\mathbb{N}$ from samples. In the case where $\mu$ is a product measure, as considered in this paper, it is fully described by a vector $p \in [0,1]^{\mathbb{N}}$. One natural estimate for $p$ is the empirical... | Rebuttal 1:
Rebuttal: Thank you for your constructive review. We appreciate your insights and address your key concerns below:
1. Parameter Tuning and Proof Clarity (Theorems 2.2 and 2.3):
We recognize that parameter tuning in Theorem 2.2 is indeed the main challenge. We intend to offer clearer intuition on how the... | Summary: The paper discusses the "Local Glivenko Cantelli" problem focusing on families of product distributions. There are three main results:
The first main theorem (Theorem 2.1) argues that LGC (the family of product measures that is learnable by the empirical mean estimator EME) is the largest family learnable by ... | Rebuttal 1:
Rebuttal: We thank you for your detailed and insightful evaluation and for your positive comments regarding the clarity of our main results and the rigor of our proofs. Below, we address your specific concerns:
1. Regarding the assumption $\dot{p}_j^*\in[0,1/4]^\mathbb{N}$, note that we can disregard any v... | Summary: This paper investigates the mean estimation problem in the binomial empirical process. First, under mild technical conditions, it establishes that the LGC class, as defined in Cohen & Kontorovich (2023), is the largest class that is learnable by any estimator. Furthermore, it demonstrates that the empirical me... | Rebuttal 1:
Rebuttal: Thank you for your thorough and encouraging evaluation. We appreciate your recognition of the soundness and clarity of our theoretical contributions and proofs. Regarding the observation that the problem might appear somewhat standalone, we would like to emphasize that the original LGC paper by Co... | Summary: This paper focus on the Local Glivenko Cantelli setting, which studies the uniform convergence rates of Empirical Mean Estimator (EME).
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: No.
Experimental Designs Or Analyses: No.
Supplementary Material: No.
Relation To Bro... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We fully agree that presenting the key questions in an intuitive way is valuable. In our work, we carefully balanced the need to convey the underlying ideas with the necessity of rigorous technical proofs, given the inherent complexity of the Local Glivenk... | null | null | null | null |
Emergence in non-neural models: grokking modular arithmetic via average gradient outer product | Accept (oral) | Summary: This paper investigates grokking in the common scenario of modular arithmetic. In contrast to previous approaches, the authors show that grokking occurs when using the RFM learning algorithm. This enables them to isolate the feature learning as the source of grokking in these setups. By defining two progress m... | Rebuttal 1:
Rebuttal: We thank the reviewers for their detailed feedback. We address their questions and comments below.
>I felt that some details are still unclear and may need further investigation: What is the fundamental mechanism behind the delayed generalization observed here? In other words, why does generaliza... | Summary: This paper studies the phenomenology of learning with recursive feature machines for modular addition, subtraction, multiplication, and division. Modular addition has become a standard setting for studying grokking as an example algorithmic task and has been studied for learning with neural networks, typically... | Rebuttal 1:
Rebuttal: We thank the reviewer for their in-depth feedback on our submission. We will address their comments and questions here.
>Theorem 5.1 could come earlier in the paper and also be written formally as done in Appendix
"well-known results for multiplicative group" may not be well known to ML audience... | Summary: This paper studies the grokking phenomenon in modular arithmetic. The main idea is that grokking happens because models slowly learn the right features, not because of any specific neural network architecture or gradient-based optimization. The authors use Recursive Feature Machines (RFMs) to show that even wh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and thorough feedback. We will answer their questions below.
>While the paper is well-grounded in the literature on grokking and feature learning, it could benefit from more discussion of works on implicit bias in optimization and how these biases... | Summary: The paper focuses on "grokking" -- a phenomenon that has attracted a lot of interest recently mostly because it is not what the ML community was used to observe regarding training dynamics and generalization.
The paper's main point seems to be that grokking is not specific to neural networks and to SGD train... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We address their questions and comments below.
>First, the paper focuses exclusively on modular arithmetic -- but grokking is a more general phenomenon and it has been observed also in language models among others. This has to be clarified someho... | null | null | null | null | null | null |
Autoformulation of Mathematical Optimization Models Using LLMs | Accept (poster) | Summary: This paper proposes an approach for autoformulation—the automated creation of solver-ready mathematical optimization models from natural language problem descriptions. The authors frame autoformulation as a search problem and leverage Large Language Models (LLMs) with Monte-Carlo Tree Search (MCTS) to systemat... | Rebuttal 1:
Rebuttal: *We appreciate the reviewer’s detailed and thoughtful evaluation.*
---
### [P1] Engineering vs theory
Thank you for the thoughtful comment. Our framing of optimization modeling as a three-step process (requirements gathering $\rightarrow$ mathematical model $\rightarrow$ computational model) is... | Summary: This paper proposes a search-based autoformulation of mathematical optimization problems. The authors provide a formal definition of autoformulation and use MCTS to construct the formulation. Experiments demonstrate the method can outperform the baselines.
Claims And Evidence: The paper proposes three challen... | Rebuttal 1:
Rebuttal: *We appreciate the reviewer’s detailed and thoughtful evaluation and positive feedback.*
---
### [P1] Addressing challenges
Thank you for raising this point. However, we believe this may stem from a misunderstanding. Please allow us to clarify how our method addresses all three challenges:
| C... | Summary: This work studies the autoformulation for mathematical optimization models, or the task of building an optimization model from natural language prompts describing the problem. The approach begins by defining the construction of an optimization model as a hierarchical task, with steps of selecting parameters an... | Rebuttal 1:
Rebuttal: *We appreciate the reviewer’s detailed and thoughtful evaluation and positive feedback.*
---
### [P1] Additional analysis
Thank you for this thoughtful suggestion. While objective-value correctness is a standard metric in prior work, we agree that it is an imperfect proxy. To address this, we c... | Summary: This paper introduces autoformulation, the automated translation of natural language problem descriptions into solver-ready mathematical optimization models, addressing the reliance on human expertise in traditional modeling. The proposed method integrates LLMs with MCTS to hierarchically decompose and systema... | Rebuttal 1:
Rebuttal: *We appreciate the reviewer’s detailed and thoughtful evaluation and positive feedback.*
---
### [P1] MCTS motivation
Thank you for the thoughtful question. We interpret this as raising two related concerns: **(1)** why use a tree-based search framework at all, and **(2)** why choose MCTS speci... | null | null | null | null | null | null |
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation | Accept (poster) | Summary: This paper studies sample-efficient exploration for the multi-reward multi-policy evaluation problem, which aims to simultaneously evaluate all target policy-reward pairs. The authors use an instance-specific lower bound to guide the design of their proposed efficient exploration method. Furthermore, they prop... | Rebuttal 1:
Rebuttal: We would like to thanks the reviewer evaluating our work and their positive appreciation of our paper. Below, we address your questions:
> In my understanding, the first bullet below Proposition 4.1 suggests that if the condition is satisfied, then Alt is empty, leading to a no-confusing scenari... | Summary: This paper investigates the problem of efficiently evaluating multiple policies across multiple reward functions in an online discounted setting. The authors provide an instance-specific lower bound on sample complexity and leverages it to design an efficient exploration strategy, adapting the MR-NaS explorati... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their evaluation of our work and their positive remarks.
> The key contribution is of this paper is that this is the first work for multi-reward multi-policy evaluation. [...] However, a limitation is that the discussion is only restricted in the tabular setti... | Summary: This paper addresses the problem of online multi-reward multi-policy evaluation in reinforcement learning, aiming to estimate the value functions of multiple policies across diverse reward sets with (ε, δ)-PAC guarantees. The authors derive an instance-dependent sample complexity lower bound that scales with a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for evaluating our work and acknowledging the novelty of our paper.
Below, we address your concerns in detail.
>The communicating MDP assumption may restrict applicability to environments with transient states.
We note that our focus on communicating MDPs is a ... | Summary: This paper studies the problem of devising an optimal data-collection policy that can evaluated policies in multi-reward and multi-policy setting. The paper adopted a PAC sample complexity perspective over finite or convex set of rewards. The analysis of the problem revolves around the set of alternative set a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and the time they dedicated to evaluating our paper. Below, we provide detailed responses to each of their concerns.
> It is quite important to distinguish the work described in this work and that of (Russo \& Vannella, 2024). [...] With clear distinctions, ... | null | null | null | null | null | null |
Agent-Centric Actor-Critic for Asynchronous Multi-Agent Reinforcement Learning | Accept (poster) | Summary: This paper proposes an Agent-Centric Actor-Critic framework for asynchronous multi-agent reinforcement learning, which includes a module that addresses asynchrony without relying on padding. The proposed module incorporates agent-centric history encoders for independent trajectory processing and an attention-b... | Rebuttal 1:
Rebuttal: We appreciate your insightful comments and would like to clarify our contributions and address concerns as follows:
### **[Q1] Parameter Comparison**
We agree that comparing parameters is important, so we compared the total number of parameters between ACAC and Mac-IAICC. The results are as follo... | Summary: This paper introduces Agent-Centric Actor-Critic (ACAC), a novel algorithm designed for asynchronous multi-agent reinforcement learning (MARL) in environments with sparse rewards and varying macro-action durations. Each agent's trajectory is processed independently, capturing the history of macro-observations ... | Rebuttal 1:
Rebuttal: We are grateful for your thoughtful remarks and would like to provide clarification on our contributions and address the raised concerns.
### **[Q1,W1] Analysis of Modified GAE**
Similar to the original GAE, our proposed method does not guarantee convergence in general.
However, just as t... | Summary: This paper proposes the Agent-Centric Actor-Critic (ACAC) algorithm to address asynchronous multi-agent reinforcement learning (MARL) in sparse-reward environments with macro-actions. The key innovation lies in replacing padding-based centralized critics with agent-centric history encoders and attention-based ... | Rebuttal 1:
Rebuttal: We are grateful for your feedback and would like to offer a detailed explanation of our contributions while addressing the concerns raised.
### **[W1] Analysis of Modified GAE**
It is known that the original GAE does not guarantee convergence in general.
However, specific boundary cases are clear... | Summary: This paper tackles the challenges encountered in asynchronous multi-agent reinforcement learning (MARL) arising from the use of macro-actions with varying durations. In traditional Centralized Training with Decentralized Execution (CTDE) frameworks, a padding technique is often used to fill in missing macro-ob... | Rebuttal 1:
Rebuttal: Your valuable comments are much appreciated. In response, we aim to clarify our contributions and address the points of concern.
### **[Q1] Qualitative Analysis Request**
Thank you for the insightful suggestion to correlate attention scores with behavior.
This is an excellent direction for futur... | null | null | null | null | null | null |
Geometric Generative Modeling with Noise-Conditioned Graph Networks | Accept (poster) | Summary: The paper introduces Noise-Conditioned Graph Networks (NCGNs), a class of graph neural networks that dynamically adapt their architecture based on the noise level during flow-based generative modeling of geometric graphs. The key innovation is Dynamic Message Passing (DMP), which adjusts both the connectivit... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, effort, and constructive feedback. We address the reviewer’s concerns and questions below:
> Learned scheduler
**Implementing learnable schedules is difficult** because the schedule is used for kNN graph construction and coarse-graining procedures, which inv... | Summary: This paper introduces Noise-Conditioned Graph Networks (NCGNs), a generative modeling approach for geometric graphs that dynamically adjusts graph structure based on noise levels rather than keeping it fixed throughout the process. The authors propose a method to adapt how information flows through the graph d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, effort, and constructive feedback. We address the reviewer’s concerns and questions below:
> Essential References Not Discussed
Thank you for pointing out these references. We will ensure these works are discussed in the related works of the camera-ready ver... | Summary: This work propose to change the architecture of the backbone model according to the noise level in flow match models. It shows that the reception field should be expanded and the resolution should be coarsen in high noise level. Based on this insight, this work proposes DMP, which consistently outperforms noi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, effort, and constructive feedback. We address the reviewer’s concerns and questions below:
> In section 3.1, this work cited no work on geometric generative models
Our paper references key geometric generative modeling works in the introduction (e.g. [1-5]),... | null | null | null | null | null | null | null | null |
Mixture of Experts Provably Detect and Learn the Latent Cluster Structure in Gradient-Based Learning | Accept (poster) | Summary: The authors consider the problem of learning a mixture of $C$ tasks, plus one global task, with a mixture of two-layer network experts. They first establish that a single network is unable to recover the global signal, by constructing a special instance of this class of targets. They then turn to analyze the m... | Rebuttal 1:
Rebuttal: # Reviewer wqTg,
We sincerely thank the reviewer for their thoughtful evaluation and insightful questions.
## Q.1
As the reviewer correctly noted, the conditions for the theorem rely on the inequality $M \gtrsim C \log (C / 0.001)$ in Lemma 4.9. In connection with this, while we initially describ... | Summary: This paper theoretically studies the learning dynamics of Mixture-of-Experts (MoE) models in nonlinear regression tasks with an underlying cluster structure. The main contribution of the paper is listed below
- Proves that a standard neural network fails to detect and exploit the latent cluster structure, whi... | Rebuttal 1:
Rebuttal: # Reviewer bEDR,
We sincerely thank the reviewer for the thoughtful and constructive feedback. We will incorporate the suggestions into the final version. Below, we address the reviewer’s comments. For references, please see our response to Reviewer **wqTg** due to character limits.
## Multi-phase... | Summary: This paper presents a comprehensive analysis of the sample and computational complexity of Mixture of Experts (MoEs) when optimized using stochastic gradient descent (SGD) for a regression problem.
Claims And Evidence: Based on the data model proposed in Assumption 3.2, all the claims appear highly likely to ... | Rebuttal 1:
Rebuttal: # Reviewer VrRr,
We sincerely thank the reviewer's insightful and constructive comments. In the final version, we will separate the presentation of theoretical results and experiments, and further elaborate on the writing in the supplementary material. For the theoretical results, we will include ... | Summary: In this paper, the authors investigate the sample and runtime complexity of Mixture-of-Experts (MoE) optimized with the stochastic gradient descent when learning a regression task with an underlying cluster structure of single index models. In particular, they show that a single neural network cannot detect a ... | Rebuttal 1:
Rebuttal: # Reviewer YoCg,
We sincerely appreciate the reviewer's thoughtful and constructive feedback. Below, we carefully address the raised concerns and questions.
Due to character limits, please see our response to Reviewer **wqTg** for references.
## Model parameters
Following the reviewer's precise fe... | null | null | null | null | null | null |
SlimLLM: Accurate Structured Pruning for Large Language Models | Accept (poster) | Summary: This work involves compressing LLMs by width pruning of sublayers in transformer blocks, where MHA and FFN are treated differently. For MHA, head pruning is performed based on the similarity between outputs with and without specific heads. For FFN, channel pruning is carried out using a Wanda-based metric from... | Rebuttal 1:
Rebuttal: Q1: Proposed metric for MHA
A1: We design similarity-based approach to enhance linear fitting by maximizing output similarity. To verify the effectiveness of our similarity-based approach, we employed the fluctuation metric from FLAP to evaluate the importance of heads, while aligning other confi... | Summary: This paper proposes SlimLLM, a structured pruning method for LLMs that evaluates redundancy via Pearson similarity-driven head pruning and PCA-guided FFN channel pruning. A lightweight linear recalibration reduces post-pruning accuracy loss, while dynamic layer sparsity optimizes resource allocation. Experimen... | Rebuttal 1:
Rebuttal: Q1: This paper should be carefully proofread. For example, what is $O_{-h_{i}}$ in Figure 1? $S_{-p}$ is not easy to understand in Algorithm 1 and should be replaced by better equation format.
A1: Thank you for your suggestion. $O_{-h_{i}}$ denotes the output excluding the $i-th$ head. $S_{-p}$ r... | Summary: This paper proposes SlimLLM for pruning large language models (LLMs). The method uniquely combines Pearson correlation analysis for attention head redundancy detection with PCA-based directional importance for FFN channel pruning. A lightweight linear calibration technique minimizes post-pruning performance de... | Rebuttal 1:
Rebuttal: Q1: Computational complexity of iterative head pruning (Algorithm 1) is unaddressed, raising concerns for larger models.
A1: The computational complexity of Algorithm 1 is acceptable. First, the number of heads is generally small, which limits the number of iterations in the algorithm. Second, th... | Summary: This paper proposes a structured pruning method for large language models (LLMs) that compresses both the feed-forward (FFN) and attention layers to accelerate inference. The pruning algorithm incorporates two key techniques: (1) removing redundant attention heads based on Pearson similarity and (2) pruning FF... | Rebuttal 1:
Rebuttal: Q1: Although the proposed sub-methods have been proven effective, the relation between FFN pruning and attention layers pruning is no so clear. Are they independent methods?
A1: In this method, both the head pruning and channel pruning strategies are designed to increase the linear correlation be... | Summary: The paper presents SlimLLM, a structured pruning approach for large language models (LLMs) that tackles channel and attention head pruning through a unified importance evaluation framework. The paper introduces several novel techniques, including using Pearson similarity to identify redundant attention heads w... | Rebuttal 1:
Rebuttal: Q1: The paper focuses on pruning-based acceleration of LLMs but lacks a comparison with quantization-based acceleration methods. Including such a comparison would better demonstrate the proposed approach's advantages of LLM compressing techniques.
A1: Thank you for your suggestion. Quantization ... | null | null | null | null |
NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric | Accept (poster) | Summary: The paper proposes a data selection framework that computes the evaluation metric (e.g., reward) for validation samples and employs the Monte-Carlo policy gradient to calculate the influence of each training data point on the validation data.
Claims And Evidence: See Other Strengths And Weaknesses
Methods An... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting the importance of our work on data efficiency in instruction tuning and for finding the paper easy to understand. Below are clarifications to address your concerns:
1. Since discrepancy between NTP loss & eval. metrics has been shown in previous works (lines... | Summary: The paper proposes NICE, a RL based framework for choosing instruction tuning data targeted for downstream tasks. The proposed method uses reward signals such as loss function or influence function on the validation data. The policy gradient is then used to estimate the training data point's influence on the g... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on our writing and the thoroughness of our methods and evaluations. We wish to make the following clarification.
**Claims And Evidence:**
To clarify, our “task-agnostic” & “task-specific” settings pertain to the data preparation stage (to form the ... | Summary: This paper proposes NICE, which provides an innovative and label-efficient approach to data selection by using policy gradients for non-differentiable evaluation metrics, outperforming several baselines in many benchmarks, including both task-specific and -agnostic settings. While its computational cost and co... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our insight and our RL-based data selection approach. Please find our responses below.
**Essential References Not Discussed:**
We have included TSDS (as suggested) and an additional baseline, DSIR in Tab. C of our response to Reviewer d8As. Our method outper... | Summary: The paper introduces NICE (Non-differentiable evaluation metric-based InfluenCe Estimation) for selecting training data to improve the performance of large language models (LLMs) on specific tasks. The method leverages policy gradient techniques to optimize non-differentiable evaluation metrics directly, addre... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the effectiveness of NICE and the soundness of our methods and evaluation.
**Claims And Evidence:**
We defer the computational analysis between LESS and NICE to our rebuttal for Reviewer waTE. For BM25, it is indeed an efficient retrieval method based on l... | null | null | null | null | null | null |
WGFormer: An SE(3)-Transformer Driven by Wasserstein Gradient Flows for Molecular Ground-State Conformation Prediction | Accept (poster) | Summary: This paper proposes a transformer-based neural network architecture to predict the ground state conformation of molecules from some low quality conformation (equivalent to optimization). The encoder part of the model takes as input per-atom embeddings (depending only on atom types) and atom-pair embeddings (di... | Rebuttal 1:
Rebuttal: For your comments, below we answer them one by one.
**W1: No discussion about efficiency**
**A1:** Firstly, the inefficiency of energy-based/quantum-based methods (e.g., molecular dynamics simulation and density functional theory calculation) has already been a well-established consensus (also ... | Summary: This paper proposes a transformer architecture for optimizing molecular geometries. The network first processes node features and edge features (encoding the input geometry) via residual update blocks with a bespoke attention mechanism called WGFormer. An interpretation of the WGFormer is provided as a Wassers... | Rebuttal 1:
Rebuttal: Thanks for your positive feedback and constructive comments. Below, we resolve your concerns one by one.
**W1: Test on larger datasets.**
**A1:** As shown in Table 1 of our paper, in addition to QM9, our WGFormer also achieves SOTA performance on the Molecule3D dataset, which contains about four... | Summary: The paper introduces WGFormer, a novel model that combines the strengths of energy-based simulation and learning-based methods for predicting molecular ground-state conformations. WGFormer is built upon an SE(3)-Transformer framework and is driven by Wasserstein gradient flows. In an auto-encoding setup, the m... | Rebuttal 1:
Rebuttal: Thanks for your comments. Below, we resolve your concerns one by one.
**Q1: Comparisons with other generative models**
**A1:** Existing generative models generate multiple conformations by sampling. To make such models (e.g., GeoDiff [2] and TorsionDiff [3]) applicable for generating molecular g... | Summary: this work proposes "Wasserstein gradient flow-driven" transformer, to gradually refine a initial 3D conformation to its ground state.
this refinement process is associated with minimizing an energy function, thus enhancing its interpretability, and probably explains its performance improvement.
the method is v... | Rebuttal 1:
Rebuttal: Thanks for your appreciation of our work. Below, we resolve your concerns one by one.
**Q1: Visualize the latent energy function values achieved through different numbers of layers.**
**A1:** As we have mentioned in lines 30-35 of our paper, our WGFormer is a Wasserstein gradient flow-driven SE(... | null | null | null | null | null | null |
L-Diffusion: Laplace Diffusion for Efficient Pathology Image Segmentation | Accept (poster) | Summary: The paper introduces L-Diffusion, a novel approach to pathology image segmentation by leveraging Laplace distributions and contrastive learning, achieving good performance, and demonstrating generalization capabilities.
Claims And Evidence: Supported Claims:
1) $\textbf{Laplace Distributions Improve Segmenta... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer ezdp
We sincerely appreciate your efforts in reviewing our paper and for your constructive feedback. We have organized your comments and provided our responses below, hoping they address your concerns.
**[Question (Q)1] Efficiency of L-Diffusion**
Answer (A)1: Thanks. L-Dif... | Summary: This paper introduces L-Diffusion, an innovative framework designed to advance pathology image segmentation by utilizing Laplace distributions and contrastive learning. The primary contribution of the paper lies in its use of Laplace distributions to model distinct components within pathology images, which en... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer 9Pf9
We sincerely appreciate your thorough and insightful review of our paper, L-Diffusion: Laplace-Based Diffusion Model for Pathology Image Segmentation. Your positive evaluation of our contributions, including the use of Laplace distributions, contrastive learning, and the... | Summary: This paper proposes a new diffusion-based method to tackle pathology image segmentation. The pathology image segmentation is a challenging task because of the large, gigapixel resolution, diverse scales, and imbalanced tissue distributions in these images. Traditional segmentation models like U-Net and DeepLa... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer nPmf
We appreciate the reviewers' detailed and insightful feedback on our work. Below, we address the key points raised in the review.
**Response to Theoretical Claims**
We are grateful for the acknowledgment that our theoretical derivations and mathematical proofs are cor... | Summary: The paper introduces L-Diffusion, a novel Laplace Diffusion Model designed for efficient pathology image segmentation. Unlike traditional approaches relying on Gaussian distributions, L-Diffusion employs multiple Laplace distributions to better differentiate component features in pathology images. The model fo... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer ZaXb
We sincerely appreciate the reviewer's valuable feedback and insightful comments on our paper. We are pleased that the reviewers recognize our contributions in introducing L-Diffusion, leveraging Laplace distributions for pathology image segmentation, and improving segme... | null | null | null | null | null | null |
I Think, Therefore I Diffuse: Enabling Multimodal In-Context Reasoning in Diffusion Models | Accept (poster) | Summary: This paper presents ThinkDiff, a framework that can efficiently and effectively align VLMs with diffusion models. Specifically, the framework design is inspired by the findings that latest diffusion models either use LLMs (e.g., T5) or CLIP as text encoders to guide the output image/video generation, so the pa... | Rebuttal 1:
Rebuttal: **Reviewer j31i**
We thank Reviewer j31i for the insightful comments and suggestions. We provide our responses as follows and will add additional literature in the reference.
>**Q1: Why alignment?**
There are several advantages in terms of the alignment compared to text-based methods on the mul... | Summary: This paper introduces **ThinkDiff**, a novel alignment paradigm that enhances text-to-image diffusion models with **multimodal in-context reasoning** capabilities. Instead of traditional pixel-level reconstruction-based fine-tuning, the authors propose aligning vision-language models (VLMs) with the decoder of... | Rebuttal 1:
Rebuttal: **Reviewer fnpq**
We thank reviewer fnpq for the insightful comments and suggestions. We address major concerns as follows and will add additional literature in the reference.
>**Q1: Ablation on different VLMs.**
We use Qwen2-VL-7B which supports interleaved image and text inputs as the LVLM. ... | Summary: This paper proposes "ThinkDiff", a novel method to incorporate VLMs in text-to-image generation pipelines with the goal to improve multimodal understanding and in-context reasoning capabilities. The key lies in aligning the VLM outputs with the diffusion decoder input space, which is done by using the correspo... | Rebuttal 1:
Rebuttal: **Reviewer BZ6o**
We thank Reviewer BZ6o for the insightful comments and suggestions. We provide our responses below.
>**Q1: Generation quality.**
We evaluate the general image-conditioned generation of ThinkDiff-LVLM on 1k images in COCO. The models are conditioned by an image in the experimen... | Summary: The paper enables diffusion models to perform in-context reasoning across images and text, rather than just reconstructing pixel information. The paper shows two variants --- LVLMs and CLIP. The images generated are of good quality and obtain state of the art performance on various measures.
Claims And Eviden... | Rebuttal 1:
Rebuttal: **Reviewer 37KH**
We thank Reviewer 37KH for the insightful comments and suggestions. We provide our responses below.
>**Q1. Experiment with Imagebind.**
We conduct an experiment with ImageBind-style alignment to align the LVLM decoder and the T5 encoder. The input of LVLM is an image and a te... | null | null | null | null | null | null |
Normalizing Flows are Capable Generative Models | Accept (oral) | Summary: The paper proposes a new architecture called TarFlow, which is a Transformer-based variant of Masked Autoregressive Flows (MAFs). TarFlow achieves a high-performance normalizing flow (NF) model by stacking autoregressive Transformer blocks on image patches and alternating the autoregressive direction between l... | Rebuttal 1:
Rebuttal: ## Q: Motivation and difference to flow matching
A: We’d like to clarify that the exact notion of Normalizing Flow (NF) we consider is here fundamentally different from the modern notion Flow Matching (FM) method. In our paper, we follow the conventional notation of NFs that exclusively refer to ... | Summary: The paper presents a normalizing flow architecture and training pipeline for image generation that significantly improves previous normalizing flow models and obtains competitive performance when compared with diffusion models and GANs. The architecture uses a masked transformer backbone to implement RealVPN-t... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contributions and we agree most of your assessments.
## Q: Limitations of likelihood-based models
A: An interesting point of discussion the reviewer brought up is the fundamental limitations of likelihood-based models. We agree with the reviewer that... | Summary: This paper proposes to integrate visual transformer architecture into Real NVP Normalizing Flows. Over the past years normalizing flows has been inferior to other types of generative models; particularly when compared to diffusion models. This paper claims that the reason for that is the design limitation of n... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review, and please see our responses below.
## Q: Scalability claim
A: Our claim about the scalability of TarFlow is within the context of different model sizes and training FLOPs on a given dataset, which is supported by evidence in Sec 3.5 and Figure 6.... | Summary: This paper scales up masked autoregressive flow (MAF) with powerful transformer architecture along with several techniques such as classifier guidance, noise augmentation and achieve good performance on many datasets including high resolution AFHQ and multimodal dataset Imagenet.
## update after rebuttal
The ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our contributions and we answer each of the questions below.
First of all, please note that we do have a supplementary material where we include more experimental settings, related work and results which we believe might be interesting to the reviewer.
... | null | null | null | null | null | null |
Structure Is All You Need: Structural Representation Learning on Hyper-Relational Knowledge Graphs | Accept (poster) | Summary: This paper presents MAYPL, a novel structure-driven representation learning method for hyper-relational knowledge graphs (HKGs). Unlike existing methods that rely on transformers or GNNs with limited structural utilization, MAYPL fully exploits the structural properties of HKGs to achieve state-of-the-art link... | Rebuttal 1:
Rebuttal: >I believe HittER is also quite relevant to this work. Hitter (Hierarchical Transformers for Knowledge Graph Embeddings) (Chen et al, EMNLP 2021) is a transformer-based model specifically designed for hyper-relational knowledge graphs (HKGs). Instead of relying on GNNs or embeddings, HittER models... | Summary: This paper presents MAYPL, a structure-driven representation learning method for hyper-relational knowledge graphs. MAYPL contributes a structure-driven initializer and attentive neural message passing to learn entity and relation representations. The method is designed to handle transudative and inductive inf... | Rebuttal 1:
Rebuttal: **Q1**\
Message passing is a fundamental concept underlying most existing GNNs for structural learning. Therefore, GNNs are not distinguished by their adherence to the message passing paradigm but by how they compute messages and update representations. MAYPL presents its unique way of encoding me... | Summary: This paper proposes MAYPL, a GNN-based method designed for inductive reasoning on hyper-relational knowledge graphs (HKGs), a specific variant of knowledge graphs. MAYPL initializes representation vectors based on the HKG structure and utilizes a structure-driven message-passing mechanism, enabling it to perfo... | Rebuttal 1:
Rebuttal: >The evaluation of the proposed method(designed for HKGs) on normal KG inductive datasets raises concerns about fairness. Baselines like NBFNet and RED-GNN are not specifically designed for relation inductive reasoning, making the comparison less equitable. To better demonstrate superiority in ind... | Summary: The paper proposes a structure-driven representation learning method for hyper-relational knowledge graphs (HKGs). Traditional knowledge graph models extend simple triplets into hyper-relational facts by incorporating qualifiers, but many existing methods fail to effectively utilize the structure of HKGs. The ... | Rebuttal 1:
Rebuttal: **C1**\
While the input of many HKG methods is only an HKG itself without extra features, most existing HKG methods (e.g., GRAN, Hy-Transformer, and HyNT) consider an HKG as a set of individual hyper-relational facts and feed each fact into a transformer independently, disregarding the interconnec... | null | null | null | null | null | null |
Understanding the Kronecker Matrix-Vector Complexity of Linear Algebra | Accept (poster) | Summary: - The paper addresses several fundamental linear algebraic problems in the Kronecker matrix-vector query model
- Its main contribution is a proof that it requires at least an exponential number of Kronecker matrix-vector products to get a decent estimate of key properties of the matrix
- It also proves that ... | Rebuttal 1:
Rebuttal: Thanks for the positive feedback, and for catching the typo in Lemma 8 – we will correct it!
* **On the practical impact:** Our exponential ($\exp(q)$) lower bounds for the generic KMVP model serve as a crucial baseline, demonstrating that applications requiring efficiency *cannot* treat the tens... | Summary: This paper studied the Kronecker matrix product oracle complexity lower bounds for estimating the trace and spectrum of a matrix $A$. The authors showed that for a matrix $A\in R^{n^q\times n^q}$ and vector $x = \otimes x_i$, $x_i\in R^d$ and $i\in [q]$, to estimate $tr(A)$ or $\lambda_1(A)$, it requires at le... | Rebuttal 1:
Rebuttal: We thank the reviewer for their appreciation of our work and the insightful question!
* **On the effect of sparsity:** If $A$ is sparse, we do not particularly expect this to drastically reduce the *oracle* query complexity. Suppose we aim to show that $s$-sparsity reduces complexity. We can take... | Summary: Given a matrix $A$, this paper considers estimating the top eigenvalue and the trace of $A$ by matrix-vector multiplications in which the vector is the Kronecker product of $q$ vectors. The main results of the paper are that constant factor estimation of these values require exponential in $q$ such matrix-vect... | Rebuttal 1:
Rebuttal: We thank the reviewer for raising this critical point about the relationship between our general matrix lower bounds and specific tensor decomposition applications. We understand the concern that our worst-case instances might not be compactly representable. However, our work's primary focus is on... | Summary: The authors study a computational model where a matrix A can only be accessed through matrix-vector products Ax where x has the specific form of the Kronecker product of q vectors. The paper establishes several key results:
- The authors prove exponential lower bounds (in terms of q) on the number of queries n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive comments and insightful questions!
* **On subclasses with non-exponential complexity:** That's an excellent question exploring the boundary of our worst-case results. Information-theoretically, if a matrix class $\mathcal{A}$ has $D$ degrees of freedom (e.... | null | null | null | null | null | null |
Measuring Diversity: Axioms and Challenges | Accept (poster) | Summary: This paper examines in depth the problem of quantifying diversity for a set of objects, a concept widely used in various fields such as image generation, molecule generation, and recommendation systems. The authors conduct a systematic review of existing diversity measures and highlight their undesirable behav... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and support! We sincerely appreciate that you acknowledge theoretical soundness of our research and its relevance to practice. | Summary: This paper studies the problem of diversity measurement and proposes three axioms—monotonicity, uniqueness, and continuity—as necessary conditions for a reliable diversity measure. The authors analyze existing diversity measures and demonstrate that none satisfy all three axioms. To address this gap, the paper... | Rebuttal 1:
Rebuttal: Thank you for your suggestions and positive feedback! We appreciate that you find our approach novel, well-structured and theoretically sound. We address the raised concerns below.
> The paper does not explore approximations or alternative methods that balance theoretical soundness with practical... | Summary: This paper explores how to quantify diversity for a set of objects. The authors first review existing diversity measures, showing that they can have undesirable behaviors. To address this, the paper suggests three properties that a diversity measure should have: monotonicity (diversity should increase as pairw... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and positive feedback!
We see that the main concern is regarding the necessity of the continuity axiom, so let us elaborate on this subject.
**Continuity axiom**
First, it is natural to assume that minor changes in object locations should lead to small ch... | Summary: This paper discusses metrics for measuring diversity in various applications. The authors review existing diversity measures and highlight their limitations in corner cases. They propose three key properties—monotonicity, uniqueness, and continuity—that a reliable diversity measure should possess. The paper de... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions! We address the concerns below and will be happy to discuss any of the raised issues further.
> Continuity being the new axiom, I am not fully convinced with the example that it is important (mentioned in Appendix A)
Regarding the importance of the cont... | null | null | null | null | null | null |
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery | Accept (poster) | Summary: The paper benchmarks several representative neural causal discovery methods in a coherent and charitable way, revealing consistent shortcomings. These are attributed to faithfulness violations, even in large sample sizes and small graphs, suggesting a more fundamental flaw in the neural causal discovery paradi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the Reviewer's positive feedback and thoughtful assessment of our work. We are glad that our benchmarking of representative neural causal discovery methods was recognized as both coherent and charitable, providing a clear and systematic evaluation of their limitations. We a... | Summary: This paper claims that while neural causal discovery methods have become more scalable, they fundamentally struggle with accuracy when identifying true causal relationships. Neural networks are unable to reliably distinguish between real and non-existent causal connections in finite samples, and violations of ... | Rebuttal 1:
Rebuttal: We appreciate the Reviewer's thoughtful comments and acknowledgment of our work's importance in understanding neural causal discovery limitations. We admit shortcomings in the presentation, including those pointed out by the Reviewer. We have made a substantial effort to improve the quality.
Belo... | Summary: This work critically examines the limitations of neural causal discovery methods, revealing their fundamental inability to distinguish causal relationships in finite-sample regimes reliably. Through a systematic benchmarking protocol, the authors demonstrate that even state-of-the-art neural approaches struggl... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful feedback and their positive evaluation of our work. We are especially grateful for the recognition of the strong empirical evidence supporting our claims and rigorous controlled experiments that validate our approach. It is encouraging to hear that... | null | null | null | null | null | null | null | null |
GuardAgent: Safeguard LLM Agents via Knowledge-Enabled Reasoning | Accept (poster) | Summary: This paper proposes GuardAgent, a new framework designed to safeguard LLM agents by checking if their actions satisfy specific safety guard requests. GuardAgent has two main steps: 1) analyzes safety guard requests and generates a task plan. 2) Then, convert this plan into the code and execute it. The authors ... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your positive feedback on our contributions. Please find our detailed responses to your comments below.
**W1: Generalizability of GuardAgent needs to be further explored**
**A1**: Thank you for the comment. In Appendix P, we have included an application of G... | Summary: This paper proposes a framework that safeguards LLM agents by using an agent (GuardAgent) to check whether their actions comply with safety requirements. GuardAgent uses a two-step process: generating a task plan based on safety requirements, then converting this plan into executable guardrail code. The author... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your positive feedback. Please find our responses to your comments below:
**W1: The “low operational overhead” claim**
**A1**: We apologize for the ambiguity. The “low operational overhead” refers to one of the three key advantages of GuardAgent – it “employ... | Summary: This paper proposes GuardAgent, a novel framework to safeguard LLM agents by leveraging knowledge-enabled reasoning. The approach involves a two-step process where an LLM generates a detailed task plan from safety guard requests and then produces executable guardrail code via in-context learning with retrieved... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your positive feedback. We especially thank you for recognizing the importance of our work and our contributions. Please find our responses to your comments below.
**W1&Q1&W5: Dynamically evolving attack surfaces and more sophisticated adversarial scenarios**... | Summary: GuardAgent is the first guardrail agent designed to monitor and regulate the actions of LLM agents. It operates by leveraging LLMs to translate security requirements into executable guardrail code. A memory module is utilized to enhance guardrail performance by retrieving past task demonstrations. Experimental... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper and your positive ratings! We are glad that you acknowledge the importance of the problem we are solving and the practicality of our settings. In the following, we reply to your concerns one by one.
**W1: Novel conceptual advancement**
**A1**: Thank you for your... | null | null | null | null | null | null |
IMTS is Worth Time $\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction | Accept (poster) | Summary: This paper addresses forecasting on Irregular Multivariate Time Series (IMTS), where observation intervals are variable and there are missing values. The authors propose to use Masked AutoEncoder (MAE) to efficiently handle missing values in IMTS and scale pre-training/fine-tuning. They also propose to use a G... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and recognition of our work’s **novelty and practical impact**, addressing each point below for clarity.
## Overview of Method
VIMTS processes IMTS by: 1) segmenting into fixed intervals with variable-length points per channel; 2) extracting un... | Summary: The paper introduces VIMTS, a framework adapting MAE for IMTS prediction, addressing challenges like unaligned signals and missing values. Unlike existing methods that separately model temporal and channel patterns, VIMTS enhances representation learning by transforming sparse signals into image-like patches, ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the practical significance and our innovation for improving IMTS forecasting performance, as well as the insightful feedbacks. Below, we address each concern raised by the reviewer in detail:
## Q1: Incorrect claims regarding pre-trained models on MTS
... | Summary: This paper presents VIMTS, which exploits the ability of visually pre-trained MAEs to model semantically sparse multi-channel data for IMTS prediction. Specifically, IMTS data is treated as image-like patches across temporal and channel dimensions during the encoding process, which are divided into equally spa... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the **novelty** and our efforts to leverage the **multi-channel semantic sparse information modeling capability** and **time series domain adaption** of visual MAE. Regarding the ambiguity you pointed out and your other concerns, we address them below.
... | Summary: This paper proposes a new approach for Irregular Multivariate Time Series (IMTS), characterized by unaligned multichannel signals and massive missing values. Instead of modeling temporal and channel patterns in isolation, as in most of current research, this paper proposes VIMTS, a framework that adapts Visual... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewers' recognition of our method's **novelty**, **comprehensive experiments**, and **potential for broad application**. We address your concerns below:
## Q1: Originality and differentials from Jungo et al.
While both works explore patchification, our core innovatio... | null | null | null | null | null | null |
Weight matrices compression based on PDB model in deep neural networks | Accept (poster) | Summary: This paper studies the problem of DNN weight compression for seek of better generalization. They propose the Population Double Bulk (PDB) model to characterize the eigenvalue behavior of $\mathbf{W}^\top\mathbf{W}$ in the bulk+spikes phase during DNN training, generalizing the existing Population Unit Bulk (PU... | Rebuttal 1:
Rebuttal: Thanks for your recognition and the valuable suggestions. Please find our response below.
(**T** for Theoretical Claims, **W** for Weakness)
**1. Detailed proof of Theorem 3.4 [T]**
We are sorry for the overly simplified proof of Theorem 3.4 in our paper. We will include more detailed and con... | Summary: The paper introduces the population double bulk (PDB) model, an extension of the population unit bulk (PUB) model, to provide a more accurate description of the spectral properties of the weights in deep neural networks. In the PDB model, the informative components of the spectrum are captured by the spikes an... | Rebuttal 1:
Rebuttal: Thanks for your recognition and the valuable suggestions. Please find our response below.
(**C** for Claims And Evidence, **E** for Experimental Designs, **W** for weakness, **Q** for Questions)
**1. Whether the ratio is the best [C]**
Our primary goal is to identify the noise-information boun... | Summary: The paper proposed a population double bulk model for compressing weight matrices in neural networks. Compared to previous pupulation unit bulk model, the PDB model has more parameters and better approximates the weight matrices. Theoretical analysis (drawing tools from random matrix theory) and algorithms are... | Rebuttal 1:
Rebuttal: Thanks for your recognition and the valuable suggestions. Please find our response below. (**W** for weakness)
**1. Model details [W1]**
The entries of the **initial** weight matrix $W_0$ are **i.i.d.** with mean 0 and variance $\sigma^2$, resulting in $\mathbb{E}W_0^TW_0=\sigma_0^2 I_p$. As **... | Summary: This paper presents the Population Double Bulk (PDB) model, an extension of the Population Unit Bulk (PUB) model, for the effective compression of deep neural network weight matrices. The authors leverage a dual-cluster structure to more accurately analyze the eigenvalue distribution and employ the PDBLS algor... | Rebuttal 1:
Rebuttal: Thanks for your recognition and the valuable suggestions. Please find our response below.(**T** for Theoretical Claims, **W** for weakness, **Q** for Questions)
**1. Comparison with additional methods [W1]**
We include additional experiments on two other methods:
1. naive SVD (using 0.55 ... | null | null | null | null | null | null |
Improving the Diffusability of Autoencoders | Accept (poster) | Summary: This paper finds that the pre-trained VAE for visual generation exhits larger high-frequency components than the original RGB images, in the lens of spectral analysis using 2D DCT.
To improve the latent diffusion generative modeling, the authors proposed to align the spectral property of image latents with th... | Rebuttal 1:
Rebuttal: We deeply appreciate the reviewer’s valuable feedback and constructive recommendations. Below, we systematically respond to each issue highlighted. We will ensure comprehensive incorporation of all suggestions into our manuscript.
> It is not clear why high-frequency components have higher dimens... | Summary: The paper observes higher frequency component in VAE's latent space than those in normal RGB images and these high frequency components have greater magnitude with larger channels and stronger KL regularization. Therefore, it proposes a novel regularization technique -- scale equivariance (SE) -- to improve th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable remarks. Below, we address each raised concern.
> Why high-frequency components have higher dimensionality?
We agree this could be clearer. By "low-frequency components," we mean DCT coefficients required to reconstruct a feature map downsampled by factor... | Summary: This paper explores the latent spaces of autoencoders within latent diffusion models (LDMs), specifically examining spectral discrepancies between latent and RGB spaces. The authors introduce the concept of diffusability, which quantifies how effectively a distribution can be modeled by a diffusion process. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments, which have greatly improved our work. Below, we address each concern.
> Ambiguous caption in Figure 2
Figure 2 shows spectra of latent codes from real images encoded with from-scratch trained FluxAE with varying bottleneck sizes. We clarifie... | Summary: The authors analyze the latent space of autoencoders widely used for latent diffusion models and identify that the spectrum of autoencoders typically deviate from that of natural images. In particular, latent spaces have stronger high frequency components compared to RGB images. These high frequency components... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough feedback. In what follows, we carefully respond to each of the points raised. All comments and suggestions will be fully reflected in the revised manuscript.
> From-scratch training
Our main motivation of fine-tuning instead of training from scr... | null | null | null | null | null | null |
Skip the Equations: Learning Behavior of Personalized Dynamical Systems Directly From Data | Accept (poster) | Summary: The paper tackles the modeling of personalized dynamical systems — that is, dynamical systems whose trajectory evolves conditioned on a set of static parameters, such as the initial condition in ordinary differential equation (ODE) systems, together with other "personal" covariates, such as one patient's weigh... | Rebuttal 1:
Rebuttal: Dear Reviewer htLB,
Thank you so much for such a comprehensive review. We deeply appreciate your time and attention spent on our paper. We are glad that you found our methodology interesting, comparisons comprehensive, and claims justified. We answer the six weaknesses in your summary, then the t... | Summary: The paper proposes EPISODE, a framework for learning the behavior of personalized dynamical systems without requiring explicit equation discovery. Instead of the traditional two-step approach of identifying ODEs and then analyzing them, EPISODE directly predicts the semantic representation from data. The paper... | Rebuttal 1:
Rebuttal: Dear Reviewer g6tH,
Thank you very much for your review. We appreciate your time and effort spent reviewing our paper. We are glad you found our conceptual framework solid, the case study well-motivated, and the comparisons comprehensive. We address your comments below.
### Reliance on inductive... | Summary: The paper proposes a method called EPISODE for learning personalized dynamical systems (PDS) without explicitly discovering ordinary differential equations (ODEs). As mentioned in this paper, traditional approaches to modeling dynamical systems involve first identifying closed-form equations and then analyzing... | Rebuttal 1:
Rebuttal: Dear Reviewer evCm,
Thank you very much for your positive review! We are glad you found our method novel and our claims well-evidenced. We reply to your questions first, and then we discuss other weaknesses and suggestions.
### Q1. Runtime complexity of the composition map training
As mentioned... | null | null | null | null | null | null | null | null |
Latent Action Learning Requires Supervision in the Presence of Distractors | Accept (poster) | Summary: This paper presents an empirical study of latent action learning in the presence of distractors. They found that latent action learning struggles with distractors, and propose several changes in architecture to improve latent action learning. Notably, they found supervision with a small amount of action labels... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and effort. We address the questions below.
> I am curious about the claim on "Quantization hinders latent action learning". it seems that this is only verified by linear probing. However, as the author mentioned, linear probing has a major limitation - it can... | Summary: Latent actions prove to be useful for efficient policy pretraining from unlabeled videos. This paper aims to enhance the quality of latent actions by removing the original information bottleneck, leveraging multi-step future observations, and predicting future states in the latent space. The authors also sugge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback. We have tried to address the concerns below.
> I have some concerns about how the paper assesses the quality of latent actions. The objective of extracting latent actions is to fully encode action information while minimizing background noise, an... | Summary: This paper focuses on the setting of learning latent actions in the presence of background distractions. The authors investigate improving upon prior latent action pretraining work with recent advances in dynamics and latent action modeling. It shows that multi-step inverse dynamics, large latent action dimens... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for their time, constructive feedback, and suggestions for additional experiments, which we found very valuable. We have tried to answer the questions below.
> Clarification: is the BC policy also only trained on up to 128 trajectories?
>
Yes, the BC baseline we ... | Summary: - The paper focuses on LAMs, which aim to infer control actions from unlabelled videos
- Here the authors note a benefit of reusing action labels from later in the pipeline to help focus (through supervision) latents on control actions
- This is most effective in the presence of 'distractors', ie non-control a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive feedback. We address the main questions below.
> Missing this, which follows a pipeline similar to LAPA: IGOR…
>
We will include the citation, thank you for your suggestion.
> … naturally a wider bottleneck will allow more information... | null | null | null | null | null | null |
On Measuring Long-Range Interactions in Graph Neural Networks | Accept (poster) | Summary: The Long Range Graph Benchmark (LRGB) is a widely adopted tool for evaluating the long-range capabilities of frameworks in long-range graph tasks. This paper identifies its limitations and introduces a formal range measure for operators on graphs, encompassing both node-level and graph-level tasks. Furthermore... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and recognition of the practical significance and credibility of our work. We address the reviewer’s concerns below. We also note that, following suggestions from other reviewers, we have performed additional experiments for GCN and GT on Cora (as ... | Summary: The paper works in the area of long-range dependencies for graph neural networks. Its main contribution is to define a new metric to measure the range of a task. This metric can also be applied to GNNs that are trained on a task, approximating the true range of the task. Experiments on the LRGB datasets indica... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback, and for appreciating the novelty of our contribution and its significance as an evaluation tool for GNNs and graph tasks. We address their concerns below. As suggested, we have performed additional experiments for GCN and GT on Cora (as a known sh... | Summary: This paper introduces a formal measure for evaluating long-range interactions in Graph Neural Networks (GNNs), addressing the limitations of existing empirical benchmarks like LRGB, which lack theoretical grounding. The proposed measure quantifies a model’s ability to capture long-range dependencies, validated... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and for appreciating our contributions. We address their concerns below. As suggested, we have performed additional experiments for heterophilous tasks, as well as on Cora, virtual node and activation function ablations for our Fig. 4 synthetic tas... | null | null | null | null | null | null | null | null |
Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner | Accept (spotlight poster) | Summary: This paper primarily aims to tackle multimodal ToM, though in practice, their main focus is still on complex multi-step reasoning tasks, with the multimodal aspect being somewhat secondary in their method.
They propose the "Weak-to-Strong Control" strategy, which modifies the probability distribution at the o... | Rebuttal 1:
Rebuttal: **We sincerely appreciate Reviewer Dtd9’s valuable comments and suggestions.**
---
**Q1:** *Comparison with fine-tuned large LMs (e.g., 70B/405B)*
**A1:**
**Table D: https://anonymous.4open.science/r/response_tom-BD87/tableDEFGH.md**
Directly fine-tuning a 405B model is practically infeasible... | Summary: This paper addresses the scalability limitation of Theory-of-mind (ToM) models in multi-modal environments. Predicting agents' goals and beliefs in complex mutli-modal environments involving vision and language requires visual understanding, multiple steps planning and reasoning, as well as extensive world kno... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer RMFT for the positive and encouraging assessment, and for clearly recognizing the unique complexity in multimodal ToM we aim to address. The core philosophy of our weak-to-strong guidance BIP framework is precisely to leverage specialized small language models to effici... | Summary: This paper proposes a scalable Bayesian Planner that employs small models for stepwise Bayesian updates, refining the likelihood estimation of larger models. Experimental results demonstrate that this approach outperforms existing methods on multimodal Theory of Mind (ToM) benchmarks and generalizes well to un... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer DnoB for the insightful comments and valuable suggestions.**
---
**Q1:** *Does direct post-training limit the generalization of the large LM, or does guidance from smaller post-trained LMs enhance generalization?*
**A1:**
Thank you for raising this insightful que... | Summary: The paper presents a Bayesian ToM method using stepwise belief updates and weak-to-strong LM transfer, unifying social and world knowledge to achieve 4.6% higher accuracy on multimodal tasks (including unseen settings) than prior approaches, resolving scalability/generalization trade-offs.
## update after reb... | Rebuttal 1:
Rebuttal: **We sincerely thank Reviewer dLAb for their insightful comments and support.**
---
**Q1:** Practicality under strict real-time/resource constraints?
**A1:**
Our method uses a small post-trained LM (4B/8B) to dynamically guide the large pretrained LM (70B/405B) at inference. Practically, both... | null | null | null | null | null | null |
Score-Based Diffusion Policy Compatible with Reinforcement Learning via Optimal Transport | Accept (poster) | Summary: The method proposed in this paper is quite complex, but I will do my best to summarize it:
* The authors attempt to integrate diffusion policies (which are typically learned via imitation on expert data) with online environment interactions.
* To this end, the authors make the following key observation (Prop 4... | Rebuttal 1:
Rebuttal: Dear Reviewer an3h,
Thank you for your detailed review and constructive feedback on our submission. Below, we address your main concerns and questions.
## C1: Claims And Evidence:
**R1:** We appreciate the feedback regarding evidence granularity. To substantiate our claims, we have conducted... | Summary: This paper introduces OTPR, a novel method that integrates optimal transport theory with diffusion policies to enhance the robustness and performance of imitation learning models through online interactions with the environment . The core algorithmic idea involves leveraging the Q-function as a transport cost ... | Rebuttal 1:
Rebuttal: Dear Reviewer ZmNb,
Thank you for your positive review and valuable feedback on our submission. Your comments have provided us with clear directions for improvement, and we are committed to addressing them in the revised version of our paper. Below, we address your main concerns and questions.
... | Summary: The paper proposes OTPR that leverages optimal transport for fine-tuning diffusion policy in RL. Q function is treated as the transport cost and the policy is considered the transport map. Masked OT with resampling is also applied to improve training stability. Experiment results show generally improved perfor... | Rebuttal 1:
Rebuttal: Dear Reviewer iuX1,
Thank you for your positive review and constructive feedback on our submission. We appreciate your recognition of our work and are glad to hear that you found our approach and experimental results valuable. Below, we address your specific comments and questions.
## C1: Qual... | Summary: This paper proposes to reformulate offline-to-online diffusion policy training with optimal transport. It views policy as a transport from the state distribution to action distribution, using the (negative) Q-function as a a transport cost and treating the policy as an optimal transport map. The authors show t... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thanks for your thorough review and constructive feedback on our submission.
## C1. Claims:
*The connection between OT and RL appears superficial, and the role of Q-learning methods requires clarification.*
**R1:** While the cliam has already garnered recognition from other re... | null | null | null | null | null | null |
NMA-tune: Generating Highly Designable and Dynamics Aware Protein Backbones | Accept (poster) | Summary: NMA-tune is a new method that incorporates dynamic information into protein design by conditioning backbone generation on the lowest normal mode of oscillation. It extends RFdiffusion as a plug-and-play component, improving the proportion of samples with high structural quality and desired dynamics. The approa... | Rebuttal 1:
Rebuttal: Thank you for carefully going through our manuscript and giving us your constructive feedback. As you mentioned, we tackle “challenges in generating molecules with both designable quality and targeted motions”, and we are happy to see you note the strength of the MD simulations evaluations. Let us... | Summary: The paper introduces NMA-tune, a plug-and-play modification to the RFDiffusion framework aimed at enhancing protein design by integrating Normal Mode Analysis (NMA)-inspired diffusion conditioning correction. The proposed method introduces a computationally efficient conditioning term that utilizes the fully d... | Rebuttal 1:
Rebuttal: Thank you for providing your constructive critique of our work. We are happy to hear that you appreciate the strong points of our paper, particularly you note “the experimental section is strong. The authors present three case-study proteins with well-documented hinge motions in the literature.” ... | Summary: This paper introduces a training-based method to address the problem of dynamic-conditioned generation of proteins. Specifically, they replace the prior-guided term with a simpler, more computationally efficient one to improve sampling speed, and they introduce a small network to learn such conditioned mapping... | Rebuttal 1:
Rebuttal: Thank you for providing your valuable feedback. Firstly, let us motivate again the usage of the trainable conditioner in NMA-tune. The analytical form of the NMA-loss that we use for loss-guidance might steer the generation into structures that have the ideal NMA-loss, but do not resemble proteins... | Summary: The paper aims to propose a solution to conditioning protein structure generation on structural dynamics. The authors define protein structure dynamics as the lowest normal modes of oscillations computed with Normal Mode Analysis (NMA) and propose an efficient strategy to incorporate this information into exis... | Rebuttal 1:
Rebuttal: Thank you for reading our manuscript in great detail. We are glad to know that you find our work well-written and you appreciated the strength of our experimental evaluation.
Thank you for pointing out that the “conducted experiments convincingly demonstrate that the proposed method outperforms ... | null | null | null | null | null | null |
Going Deeper into Locally Differentially Private Graph Neural Networks | Accept (oral) | Summary: This paper presents UPGNet, a utility-enhanced framework for locally private graph learning. The main contribution is a three-stage pipeline that generalizes local differential privacy protocols for perturbing node features, aiming to balance privacy preservation with improved learning utility. The authors ide... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions, which have significantly contributed to improving the quality of our paper. Detailed responses to each comment are provided below.
**Q1: Provides more description of the node classification task. This paper conducts experiments base... | Summary: This study introduces the UPGNET framework, which aims to protect user privacy through Local Differential Privacy (LDP) while enhancing the learning utility of graph neural networks. It innovatively proposes the High-Order Aggregator and Node Feature Regularization layers to optimize feature dimensions and nei... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions, which have significantly contributed to improving the quality of our paper. Detailed responses to each comment are provided below.
**Q1: Adding more experiments under the GNN model, e.g. GAT. While the paper provides valuable insight... | Summary: This paper aims to enhance the utility of locally differential privacy graph learning. Its theoretical analysis derives two key factors affecting the estimation error, i.e., feature dimension and neighborhood size, and concludes that reducing the effective feature dimension and expanding the effective neighbor... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions, which have significantly contributed to improving the quality of our paper. Detailed responses to each comment are provided below.
**Q1: Provide an analysis on the impact of graph density on the performance. The difference in graph d... | Summary: The paper introduces UPGNET, a utility-enhanced framework for locally differentially private (LDP) graph learning. It addresses privacy challenges in Graph Neural Networks (GNNs) by proposing a three-stage pipeline to generalize LDP protocols for node feature perturbation. Key contributions include identifying... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and suggestions. Detailed responses are provided below. The newly added figures and tables can be found in the link ※: https://anonymous.4open.science/r/3814/1.pdf
**Q1: Lack of LPGNN's implementation details**\
**R1:** The hyperparameters and optimi... | null | null | null | null | null | null |
Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up | Accept (poster) | Summary: This paper presents a novel analysis of the SCAFFOLD algorithm, a popular method in federated learning designed to address client heterogeneity. The authors show that the global parameters and control variates of SCAFFOLD form a Markov chain that converges to a stationary distribution, which allows them to est... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our paper!
We are happy that you found that the Markov chain-based analysis of Scaffold is original, and that "this originality strengthens the theoretical understanding of federated learning in stochastic settings," that proving that Scaffold has linear sp... | Summary: The paper proposes an analysis for the Scaffold algorithm, a popular method for dealing with data heterogeneity in federated learning. The authors first show that the global parameters and control variates define a Markov chain that converges to a stationary distribution in the Wasserstein distance. Leveragi... | Rebuttal 1:
Rebuttal: Thank you for your thorough and constructive feedback. We greatly appreciate your recognition of our paper’s contribution and the innovative perspective provided by viewing Scaffold using Markov chain formalism. Below, we carefully address each of your remarks:
**Claims and Evidence:**
We appre... | Summary: This paper investigates the convergence properties of Scaffold, a federated learning method designed to reduce variance among clients. By analyzing the global iterates and control variates from the perspective of a Markov chain, the study establishes a novel non-asymptotic convergence rate for Scaffold with re... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our paper and for your insightful comments.
We are happy that you found our paper "well written, and the theoretical part is mostly clear to follow."
**"I believe the theoretical results in this paper rely on some strong assumptions, such as the bounded third... | Summary: This paper studies the convergence of the SCAFFOLD algorithm under the assumptions of (a) strong convexity, (b) smoothness, (c) first-order similarity (i.e. the average norm of the difference between the gradients on each client and the avg function is bounded), (d) second-order similarity (like the former, bu... | Rebuttal 1:
Rebuttal: Thank you for the positive evaluation of our paper! We appreciate that you found the Markov chain point of view "very useful and insightful", and "helpful to the community going forward", and that you found our theory based on algorithmic stability "very creatively applied here" and "rare to see i... | null | null | null | null | null | null |
Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning | Accept (poster) | Summary: The paper investigates one-shot Federated Learning (OFL), which aims to reduce the communication costs associated with traditional multi-round Federated Learning. The authors highlight that existing OFL methods face significant challenges due to "garbage in, garbage out" issues, where inconsistent local models... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the time in reviewing. We appreciate that you consider our presentation is clear, and our method is effective and robust. Please see our detailed feedback for your concerns below.
**W1: Potential solutions and complexities of implementation.**
**Ans for W1... | Summary: Existing OFL methods focus on server-side aggregation, which falls into ‘garbage in garbage out’ pitfall. They unravel the root cause of such garbage inputs as intra-model and inter-model inconsistencies in the face of data heterogeneity. To address these, they design self-alignment local training and informat... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to review our work. We greatly appreciate you find our method is efficient and data-free. Please find our detailed responses to your concerns below.
**W1 & Q1: Ablation study on $L_{ssl}$ and $L_{proto}$.**
**Ans for W1 & Q1:** Thanks for your... | Summary: This manuscript tries to solve the 'garbage in garbage out' problem caused by inconsistent models in the one-shot federated learning paradigm. They propose FAFI, a novel OFL framework consisting of two key components: SALT for invariant feature learning and IFFI for server-side feature fusion-based inference. ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to review our work. We greatly appreciate you find the paper is well-motivated and well-demonstrated. Please find our detailed responses to your concerns below.
**W1 & Q1: Details in IFFI.**
**Ans for W1 & Q1**: Thanks for your important commen... | Summary: The paper addresses the critical challenge of model inconsistency in OFL due to heterogeneous data. They identify two key inconsistencies—intra-model and inter-model—and propose a novel framework, FAFI, which combines client-side self-aligned training (SALT) and server-side informative feature fusion (IFFI). E... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for taking the time to review our work. We greatly appreciate your recognition of the proposed method as both technically sound and high-performing. Please find our detailed responses to your concerns below.
**W1 & Q1: Applicability to resource-constrained scenario... | null | null | null | null | null | null |
STNet: Spectral Transformation Network for Solving Operator Eigenvalue Problem | Reject | Summary: The authors are interested in solving an eigenvalue problem for a differential operator.
Claims And Evidence: The authors claim to approximate the eigenvalues with higher accuracy than competing approaches, which they do, but I am not sure about the overall performance as the competition seems really terrible... | Summary: This paper focus on solving eigenvalue and eigenfunction problem. Numerical methods suffer from the curse of dimensionality. There is a tread of attacking the problem with methods of deep learning, e.g. NeuralEF [1], NeuralSVD [2], etc. The authors try to improve the existing methods in terms precision. Their ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score and your confidence. If not, please let us know your ... | Summary: The paper introduces the Spectral Transformation Network for solving operator eigenvalue problems, addressing challenges posed by high-dimensional operators. STNet uses deflation projection to remove the subspace corresponding to already-computed eigenfunctions, ensuring that the network does not converge to t... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score and your confidence. If not, please let us know your ... | Summary: This paper proposes a method to find eigenfunctions of a given operator using neural networks.
The idea is to combine ideas from numerical linear algebra to train neural networks to fit underlying eigenufnctions: (1) power method, (2) deflation projection, and (3) filter transform. The experiments are performe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to your comments as follows and sincerely hope that our rebuttal could properly address your concerns. If so, we would deeply appreciate it if you could raise your score and your confidence. If not, please let us know your ... | null | null | null | null | null | null | |
Distributed Differentially Private Data Analytics via Secure Sketching | Accept (poster) | Summary: In this paper the authors are attempting to find a way to build solutions that have utility as high as in case of central DP, but have as little assumptions as in case of local DP.
The most common example of such an approach is privacy amplification by shuffling. However, the number of mechanisms that could ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and questions.
> The discussion of shuffle model and other deployments where the central server is replaced by MPC is lacking details.
Regarding missing details on replacing trust in a central server by MPC, we made an extensive comparison in... | Summary: The authors introduce the linear-transformation model (LTM) for distributed differentially private data analytics. The main question that the authors aim to address is "what is the least expressive F need to be securely implemented such that distributed DP utility is comparable to that of the central model". I... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and questions.
We will incorporate the useful editorial suggestions about our Figures in the final version.
> What happens with lager privacy budget?
We discuss our choice of privacy budget $\epsilon$ in the experiments in our answer to revie... | Summary: The paper introduces the Linear Transformation Model (LTM), a new differential privacy (DP) trust model between central DP, where a single party is trusted, and local DP, where no party is trusted. In the LTM, a linear computation can be used to transform a set of private inputs of parties before they are reve... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and questions.
> I am not convinced that the utility analysis of Section 4 completely characterizes this gain. The term
$\alpha_S$ which impacts the accuracy of the protocol is only analyzed order-wisely, while it should be more concretely quan... | Summary: The authors present a new model for differential privacy, the linear transformation model (LTM). This model interpolates between the local model of DP, that does not require a trusted central server, and the central model of DP that does. Some other intermediate models have been proposed, the most well studied... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and thoughts.
> One modification that would be informative would be a plot of error versus privacy budget for the various methods. The privacy budgets used in the paper are 0.1 and 0.5, which are fairly narrow
Regarding the choice of privacy budgets in t... | null | null | null | null | null | null |
M³HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality | Accept (poster) | Summary: In this work, the authors introduce a technique for training agents in MARL settings using human feedback as a substitute for hand-designed reward functions. Specifically, their training pipeline involves (1) collecting human language feedback about a rollout video after a period of training, (2) using an LLM ... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer sgwG
We thank the reviewer for recognizing our pipeline as a "novel" contribution and for the positive comments on clarity and presentation. We address your concerns below.
---
### 1. Regarding the Framing of our work
Thanks for your thoughtful comments on our paper's scope a... | Summary: This paper addresses the challenge of designing effective reward functions in multi-agent reinforcement learning for complex, cooperative tasks with sparse or misaligned rewards. This paper proposes M³HF, a framework that integrates multi-phase human feedback of mixed quality into MARL by extending the Markov ... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer 7Tji
We thank the reviewer for agreeing that our "theoretical analysis demonstrate robustenss to noisy feedback," and that the experimental results demonstrate "faster convergence and higher asymptotic performance."
> Proposition 4.2 assumes zero-mean noise in feedback, but... | Summary: The paper introduces a novel framework named M3HF (Multi-phase Human Feedback for Multi-agent Reinforcement Learning), designed to address the challenges of sparse or complex reward signals in multi-agent reinforcement learning (MARL) by incorporating multi-phase human feedback, including feedback of varying q... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer PGeQ
We thank the reviewer for acknowledging our M3HF framework as an "innovative approach" with "real-world application potential" and for recognizing its "originality and innovation" in addressing "challenges of sparse or complex reward signals in multi-agent reinforcement ... | Summary: This paper introduces M3HF, a framework for integrating multi-phase human feedback of varying quality into multi-agent reinforcement learning (MARL). The authors propose a Multi-phase Human Feedback Markov Game (MHF-MG) that extends standard Markov Games to incorporate iterative human guidance. The framework u... | Rebuttal 1:
Rebuttal: ## Reply to Reviewer 6vBu
We sincerely thank the reviewer for your positive remarks on our framework’s structure, scalability, and consistent performance. We hope the following responses address your concerns.
---
### 1. Regarding the Novelty of Our Work
While existing work on LLM-based reward ... | null | null | null | null | null | null |
The Polynomial Stein Discrepancy for Assessing Moment Convergence | Accept (poster) | Summary: In Bayesian statistics, it is quite common to want to integrate some
functions with respect to the posterior distribution (e.g. the mean). When
the posterior is complicated, this has no closed form solution, and so
practitioners often resort to using MCMC samplers or diffusion samplers. In
the past decade, the... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for your valuable comments and suggestions. We will create a log/log plot, replace "Theorem 3.1" with "Corollary 3.1." and mention that property of the graph Stein discrepancy.
**(Lack of) confidence about power of PSD in the wild**
We have hi... | Summary: This paper proposes a class of monomials as test functions in kernelised Stein discrepancies, in order to speed up computations. It shows that when the target is Gaussian then the method works well.
Claims And Evidence: The claims are
* the method detects differences in the first r moments in the Bernstein-... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for your valuable comments and suggestions.
**On the BvM limit**
Proposition 3.2 explicitly states the Gaussianity assumption without mentioning the BvM limit, but we agree that this can be clarified in other parts of the text. We will reword ... | Summary: The paper proposes a Stein discrepancy metric (PSD) for hypothesis testing. Rather than attempt to target a broad set of functions, the paper argues that by restricting to polynomial moments (up to some order), one can obtain a metric that is light-weight for computation and also has better statistical power.
... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for your valuable comments and suggestions. We will correct the typographical error.
**What if I wanted to test against a specific set of functions other than those given in the paper? Is there any other example of this generating an interestin... | Summary: The authors introduce a variant of Stein discrepancy which uses bounded degree polynomials as a Stein set. Computing the proposed Polynomial Stein Discrepancy (PSD) is straightforward using evaluations of the target score $\nabla \log P(x)$ and samples from the proposal distribution $Q$. For degree $r$, evalua... | Rebuttal 1:
Rebuttal: Thank you for your time reviewing our manuscript and for your valuable comments and suggestions.
We will correct the typographical issues and the appendix as suggested.
**When does KSD outperform PSD? In Figure 1, there are no such examples, and in Table 1 setting $r>1$ is sufficient to match KS... | null | null | null | null | null | null |
Doubly Robust Conformalized Survival Analysis with Right-Censored Data | Accept (spotlight poster) | Summary: This work presents a novel conformal inference framework for survival analysis with right-censored data, motivated by the limitations of existing conformal methods for construction lower prediction bounds (LPBs) for general right-censored data. The core idea is to fit a censoring distribution and sample from t... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and encouraging evaluation.
## Accuracy of the Censoring Model
We appreciate your interest in how the censoring model’s accuracy affects performance. However, we believe there may be some misunderstanding about how best to assess this in practice.
Rather than r... | Summary: The paper proposes a doubly robust conformal inference method for constructing lower prediction bounds (LPBs) for survival times under right-censored data. By imputing unobserved censoring times using a machine learning model and calibrating survival models via weighted conformal inference, the method theoreti... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and generally positive review. We appreciate your recognition of the novelty and strength of our contributions and are happy to respond to your comments below.
## Asymptotic vs Finite-Sample Theoretical Results
You're right that Theorems 3.3 and 3.6 provide asymptotic... | Summary: This paper studies conformal inference for right-censored data, in which it generalizes prior works beyond the type-I censoring setting (i.e., when all the censoring times are observed). Its main idea is to impute the censoring times by sampling from the estimated censoring mechanism, and obtain a "synthetic"... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for your positive evaluation of our paper. We appreciate your careful reading, and we are happy to answer your two very insightful questions.
## Imputation randomness
*"Since the missing censoring times are imputed by sampling from the estimated $C \mid X... | Summary: The paper addresses the problem of constructing lower prediction bound (LPB) for survival time under conditional independent right censoring. In particular, the paper extends the approaches in Candes et al. (2023) and Gui et al. (2024) for type I censored data, which assumes that the right censoring time is al... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review. We find your feedback very helpful. While some presentation issues may have caused confusion, they are easily resolved and do not reflect inherent flaws. We respond below and will incorporate your suggestions into the revised paper.
## DR Adjustme... | Summary: This paper proposes a new conformal inference approach specifically for right-censored data, aimed at constructing lower prediction bounds (LPBs) for survival times. The method is theoretically asymptotically doubly robust and demonstrates strong empirical results, offering more informative and reliable LPBs c... | Rebuttal 1:
Rebuttal: Thank you for your review and your positive evaluation of our paper. We’re grateful for the opportunity to clarify some aspects of our empirical analysis, particularly since many important results are presented in the appendix and may have been unintentionally overlooked.
The appendix contains an... | null | null | null | null |
Training Deep Learning Models with Norm-Constrained LMOs | Accept (spotlight poster) | Summary: This paper develops an algorithmic framework that can exploit and appropriate choice of norm for entire neural network with emphasis on hyperparameter transfer across model sizes. The algorithm called uSCG, unconstraint stochastic conditional gradient method, shows improvements both theoretically and practical... | Rebuttal 1:
Rebuttal: We are happy that the reviewer found the paper insightful.
> "Since you build on top of modded-nanoGPT, I have to ask the question: what is your speedrun result against Muon?"
The speedrun configuration is the 124M parameter model with 512 batchsize in Fig. 1.
In this setting Scion consistently ... | Summary: The paper introduces the unconstrained Stochastic Conditional Gradient method (uSCG), which builds upon classical Conditional Gradient methods by utilizing linear minimization oracles (LMOs) even for unconstrained optimization problems. This approach leverages the geometry of the problem by assigning different... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback.
We believe there are important misunderstandings that we address below.
> "The algorithmic descriptions in algorithm 1 and 2 are same. Is this a typo?"
This is not a typo: the two algorithms are distinct and solves different problems as detailed in Secti... | Summary: The authors propose a new stochastic family of algorithms exploiting the so-called Linear Minimization Oracle (LMO) to solve optimization problems. It provides a more general framework and unifies many other algorithms as special cases. Theoretical guarantees as well as significant speed-up on the training of ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback.
> "To be verified: In Lemma D.1, I think it is the right constant is $L_2$, and not $L$."
The right constant is $L$, since smoothness is taken w.r.t. the arbitrary norm $\Vert\cdot\Vert$.
This is what eventually yields the dependency on $L$ seen... | Summary: This paper proposes a new approach to optimizing DNNs that leverages the Linear Minimization Oracle (LMO) over norm-constrained sets. The core idea is to adapt the optimizer a priori to the geometry of the neural network, rather than relying solely on on-the-fly adaptive methods like Adam. The authors introduc... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and address the remaining concerns below.
> "The CIFAR-10 experiments show the transferability of the optimal stepsize, but they do not establish the method's competitiveness in image classification"
To establish competitiveness on image classification we... | null | null | null | null | null | null |
Understanding and Improving Length Generalization in Recurrent Models | Accept (poster) | Summary: The paper investigates why recurrent models fail to generalize to sequence lengths beyond their training context and proposes methods to improve length generalization. They also propose a metric (Effective Remembrance) which basically captures the difference in a model's next token distribution at a point, whe... | Rebuttal 1:
Rebuttal: We are encouraged to see that the reviewer thinks that our unexplored states hypothesis is well supported and that our interventions on the initial state are useful to achieve length generalization. We provide responses to their questions:
> RE: Results on long-context generalization benchmarks b... | Summary: The authors study length generalization in recurrent models. They begin with an empirical analysis of length generalization failures for Mamba v1 / v2 and gated linear attention. They define a new metric, "Effective Remembrance", to quantify the influence of the context prefix on a model's predictions, and sho... | Rebuttal 1:
Rebuttal: We thank the reviewer for its thoughtful analysis of the paper and concrete suggestions, and we are encouraged to see that they found the experiments sounds and the paper easy to follow. We provide responses to their discussion:
> RE: Perplexity as an evaluation metric
We understand the reviewer... | Summary: The paper explores the reasons for limited length generalization of recurrent (mainly modern linear ones like mamba, GLA) neural networks.
The paper explores the hypothesis that this is due to "unexplored states" - i.e. the kind of states that occur after long context tend to be unfamiliar to models trained on... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed response and we appreciate that they find most our claims well supported, including our analysis on how state passing and TBTT enable length generalization because they simulate realistic states. We provide answers to their questions and observations:
> RE... | Summary: The authors in this paper propose a framework to analyze the problem of length generalization in recurrent networks. The authors primarily focus on State Space Models and how they behave when test sequences are significantly longer than training sequences by studying their response to 4 training interventions:... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and helpful discussion around length generalization in recurrent models more broadly. We are encouraged to see that the reviewer believes this type of work is relevant and that our training intervention to the initial states of SSMs are sound. We p... | null | null | null | null | null | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.