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iN2V: Bringing Transductive Node Embeddings to Inductive Graphs | Accept (poster) | Summary: This paper proposes an inductive way to learn node features from the graph structure. Specifically, it proposes to to extend node2vec to the inductive setting with the key idea being to set the embeddings of the testing nodes to be the average embeddings of their training neighbors.
Claims And Evidence: - A q... | Rebuttal 1:
Rebuttal: Question:
about the induced subgraph of a training node as mentioned in section 3.1.
Even if the testing nodes are masked, their neighborhood are still observed to some extent right (especially given figure 1)? since testing nodes may be connected to training nodes? if so then is this really indu... | Summary: The paper proposes inductive node2vec (iN2V), a procedure which updates node embeddings generated by methods like node2vec which form node embeddings based on graph topology, to account for updates to the graph topology like new nodes and edges. The core idea of the update is similar to feature propagation, bu... | Rebuttal 1:
Rebuttal: Question: [...] make a more targeted claim than the broader claim made in the abstract and introduction that iN2V provides a lift relative to N2V in the inductive setting,
[...]
In particular, a claim of gain relative to feature propagation should be specified upfront in terms of: 1) homophilic ... | Summary: This paper introduces an inductive approach to adapt shallow node embeddings for predicting unseen nodes that have at least one edge connected to the training graph. The proposed inductive node2vec method integrates post-hoc processing of node embeddings alongside corresponding adjustments to the training proc... | Rebuttal 1:
Rebuttal: Question:
I would strongly recommend incorporating additional GNN baselines such as GIN and GAT, rather than relying solely on GraphSAGE.
Answer:
The focus of the experiments is on evaluating the trained embeddings. For this, we chose MLP and GraphSage as representative models. Other models mig... | null | null | null | null | null | null | null | null |
Heterogeneous Label Shift: Theory and Algorithm | Accept (poster) | Summary: This paper introduces Heterogeneous Label Shift (HLS), a novel challenge in cross-modal knowledge transfer where both feature spaces and label distributions differ. It presents a new error decomposition theorem and a bound minimization framework to separately tackle feature heterogeneity and label shift. The a... | Rebuttal 1:
Rebuttal: Q1. Overall, it feels like there is a hole that needs to be filled between the entangled importance weight and HFA condition, that is, what happens when HFA is not perfectly achieved (which is almost surely the case), should we change the $\mathbf w$ estimation strategy and estimation characteriza... | Summary: The paper introduces the concept of Heterogeneous Label Shift to address cross-modal knowledge transfer challenges, where both feature heterogeneity and shifted label distributions affect model performance. It presents an error decomposition theorem and a bound minimization framework that tackle these issues. ... | Rebuttal 1:
Rebuttal: Q1. What is the primary role of the unlabeled parallel instances, and would the proposed method become inapplicable if these data were lacking?
A1. Thank you for your insightful question. As the second response to the Reviewer SzhU, parallel instances establish a cross-modal channel that facilita... | Summary: The paper introduces a new Heterogeneous Label Shift (HLS) method to tackle heterogeneous label shift. After analyzing the impact of feature spaces and label shift, the authors propose a new error bound. They show, with experiments on 2 real-life datasets, the efficiency of their method for multi-modal data.
... | Rebuttal 1:
Rebuttal: Q1. Typos and suggestions.
A1. Thank you for your valuable feedback. We have corrected the typo in the manuscript and checked it carefully throughout. In addition, as suggested by the reviewers, we added a brief summary of the comparison methods, and the related work section has been moved earlie... | Summary: This paper introduces Heterogeneous Label Shift (HLS), a problem where cross-modal knowledge transfer must address simultaneous heterogeneous feature spaces and shifted label distributions. The work presents a theoretical error decomposition, proposes a bound minimization framework (HLSAN), and validates it em... | Rebuttal 1:
Rebuttal: Q1. How does HLSAN scale with the number of classes ($k$)? Does the RQP estimator degrade for $k > 100$?
A1. Thank you for your thoughtful question. Based on our theoretical analysis (Theorem 3.8) as shown in the following formula, the weight estimation error is sublinear with respect to $k$ so ... | null | null | null | null | null | null |
Topological Signatures of Adversaries in Multimodal Alignments | Accept (poster) | Summary: This paper proposes to measure the topological properties of multimodal alignment from the perspective of image-based adversarial attacks. It introduces two novel topological contrastive losses, based on total persistence and multi-scale kernel methods, to quantify the topological distortion caused by attacks.... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback.
In response to the reviewer's query about attacks on text modality, we conducted additional experiments. In particular, we studied the _confusion prompts_ (also referred to as _cross-class prompt injection_ attacks) [Refs. 1 and 2 below]. We trac... | Summary: This paper aims to detect adversarial attacks against multimodal image encoders like CLIP and BLIP, where attackers introduce adversarial perturbations in the image domain to cause misalignment in the text domain (e.g., misclassification). The hypothesis is that, since the primary goal of adversarial attacks i... | Rebuttal 1:
Rebuttal: We thank the reviewer for the careful examination and thoughtful feedback. We address the following 3 reviewer's concerns:
## Q1: Number of adversarial and the size of the hold-out data
We agree they are critical factors:
1. **Number of adversarial samples**:
Detection performance depends ... | Summary: The authors consider adversarial attacks that target multi-modal systems relying on alignment of embeddings (the embeddings produced on text and image inputs are supposed to be close to each other, and the attacker supplies, e.g., an image whose embedding is close to an embedding of a wrong, non-matching text... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and insightful comments. We fully agree with the reviewer’s suggestions, including:
1. Explicitly mentioning that the Multi-scale Kernel (MK) loss is quadratic,
2. Replacing the term *"homologies"* (line 200) with *"topological summary"* to avo... | Summary: The paper explores the vulnerability of multimodal machine learning models (such as CLIP and BLIP) to adversarial attacks. It introduces novel Topological-Contrastive (TC) losses—Total Persistence (TP) loss and Multi-scale Kernel (MK) loss—to analyze how adversarial attacks affect image-text alignment. Through... | Rebuttal 1:
Rebuttal: We thank the reviewer for careful feedbacks. We address the concerns of the reviewer via the 3 following topics:
## Q1: Adaptive attack against topological-based detection
Due to the broad landscape of adaptive attacks, we focus this discussion on _gradient-based attacks_ against: an attacker re... | Summary: In this paper, the authors propose Topological Loss Functions for adversarial detection in multimodal data. The papers begins by discussing the required background. In Section 3, the authors present preliminary evidence that topological features are distinguishing to detect adversaries for multimodal data. The... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and careful examination of the manuscript. We agree with the reviewer’s comments regarding grammatical issues and typos, particularly the corrections at **Line 104** and in **Algorithm 1** (indeed, $\nabla_X$ should be corrected to $\nabla_Y$).
We... | null | null | null | null |
Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective | Accept (spotlight poster) | Summary: This paper proposes a novel method to address the fairness issue in medical image segmentation by incorporating control theory to handle data distribution disparities. By introducing distribution-aware fairness learning, the method is able to reduce unfairness among different groups while maintaining model per... | Rebuttal 1:
Rebuttal: >**R3-W1.1** The Patchify (Section 3.2) fails to clearly explain the implementation.
- Thanks for your careful comments. Patchify flattens the 4D intermediate image embeddings ‘h’ in [H, W, Z, Ch] from CNN blocks to 2D flattened embeddings ‘\tilde{h}’ in [N, Ch], where N corresponds to H x W x Z, ... | Summary: The paper "Distribution-aware Fairness Learning in Medical Image Segmentation From A Control-Theoretic Perspective" explores the issue of fairness in medical image segmentation, particularly in cases where demographic and clinical factors contribute to biased model performance. The authors argue that biases in... | Rebuttal 1:
Rebuttal: >**R2-W1** Limited external validation and dataset diversity, which may not fully capture the diversity of real-world clinical settings. The prostate cancer test set in particular is relatively small, with only 132 test samples.
- Thanks for your constructive feedback. For the prostate cancer test... | Summary: The paper proposes a distribution-aware image segmentation framework inspired by the control theory in mode switching and closed loop control. The framework incorporates the mixture of expert to address the heterogeneous distributions in medical images. Experiments in two 2D image benchmarks and a 3D in-house ... | Rebuttal 1:
Rebuttal: >**R1-W1.1** How is dMoE more distribution aware compared to a normal MoE?
- Thanks for the reviewer’s insightful point. The improved "distribution-awareness" of dMoE stems from its subgroup-aware gating, whereas a normal MoE employs per-sample gating. This design enables each gating to better cap... | null | null | null | null | null | null | null | null |
Accelerated Diffusion Models via Speculative Sampling | Accept (poster) | Summary: This paper proposed a fast sampling method for diffusion models, inspired by the idea of speculative decoding from LLMs. Using a more compact draft model to efficiently generate an image sequence and then verify the whole sequence in parallel with the original diffusion model, the sampling latency can be short... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive assessment of our work.
> This paper shares a similar idea to speculative decoding for LLMs [1], but to my knowledge, it is the first to apply this idea to diffusion model sampling. T-stitch [2] leverages the idea of using a smaller draft mo... | Summary: This paper extends the method of speculative sampling, which has been used in the context of speeding up inference for autoregressive models, to diffusion models. Roughly, this is a method by which one uses a weaker "draft" model to propose sampling steps which are then accepted with some probability by a stro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their insightful comments.
> how to think about $g_t$ [...] for classical schedules.
Let $A(t) = g_t^2 ((1/\sigma_t - \sigma_t)^2 + \alpha_t^2)$ and consider a few schedules:
* Rectified flow [1]: $\alpha_t = 1-t$, $\sigma_t = t$. We have that $A(t) = 2... | Summary: The paper provides an efficient way to apply speculative decoding from literature of language models to diffusion models, which is challenging because diffusion models use the gaussian distribution instead of discrete distribution. They address this challenge via adjusted rejection sampling with reflection cou... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their very positive assessment of our manuscript.
> Overall, their claims are well-supported by theory and experiments. Thus I think the contributions of the paper are solid and beneficial to the community of both speculative decoding and diffusion models.... | Summary: This work introduces a speculative sampling method for efficient diffusion models using reflection maximal coupling. Instead of relying on a separate draft model, they propose an approach that generates drafts directly from the target model. A complexity analysis establishes a lower bound on acceptance ratios.... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their positive assessment of our paper.
> The approach assumes parallel evaluation allowing lower latency, but the method increases compute and memory requirements, which are also critical components that are not handled by the approach.
Our approach ind... | null | null | null | null | null | null |
A Model of Place Field Reorganization During Reward Maximization | Accept (poster) | Summary: This paper develops a reinforcement learning (RL) model to explain how hippocampal place fields reorganize during reward-based navigation. In the model, Gaussian radial basis functions (place fields) receive continuous spatial inputs, and feed into an actor-critic framework that learns to navigate in 1D and 2D... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We hope our response clarifies most of the concerns.
>proposed model will highly rely on reward.
While the current model proposes a reward dependent objective, we have also proposed a non-reward-dependent objective (Metric Representation) which recapitu... | Summary: In this work, the Authors develop a reinforcement-learning model of the place field organization. They consider three effects that have been observed regarding the place fields in biology (a higher density at reward locations, an elongation backwards along the trajectory, and a drift observed at the times of s... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We hope our response clarifies most of the concerns.
>place fields in biology are organized similarly
We wanted to ask if a single, simple normative model can recapitulate several learning-induced changes in place field representations. We agree that the... | Summary: This paper proposes a model that is inspired by place fields in the hippocampus and how it could be used to develop representations that can be used for reinforcement learning. The authors argued that their model aligns with phenomena observed in neuroscience experiments, specifically high density of activity ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We hope our response clarifies most of the concerns.
>The writing of the paper is very dense
We will reduce the density to keep the description (e.g. parameter choices in methods, description of path integration based TD error in section 4.2, and figur... | null | null | null | null | null | null | null | null |
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees | Accept (poster) | Summary: This paper introduces an approach to enhance the generalization of GNNs across diverse tasks, which typically vary in their inductive biases, such as node classification, link prediction, and graph classification. The authors propose the concept of Task-Trees, a framework designed to align task spaces at diffe... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thorough and thoughtful feedback. We appreciate the recognition of our contributions and also value the constructive suggestions. We address each point in detail below.
> Theory
>
**Q1: Imprecise Task-Tree Generality Assumption**
**A1:** The generaliti... | Summary: The authors propose a various task alignment method based on task trees.
Claims And Evidence: See weaknesses part.
Methods And Evaluation Criteria: The evaluation criteria makes sense for the problem.
Theoretical Claims: I check the correctness of theoretical claims.
Experimental Designs Or Analyses: I che... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the soundness of our theoretical claims, evaluation criteria, and experimental setup. We address each point in detail below and are committed to improving clarity and rigor in future revisions.
> More Related Works and Comparative Results
>
We thank the r... | Summary: The paper proposes a graph foundation model called GIT for multiple graph learning tasks across various domains. GIT introduces task-trees as basic learning instances to align task spaces (node, link, graph) on graphs, acquire transferable knowledge, and effective adaptation to downstream tasks. A series of t... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive and encouraging feedback. We appreciate the recognition of our contributions in theory, methodology, and experimentation, as well as the thoughtful questions regarding model efficiency and fair comparisons—we have addressed these points in detail be... | Summary: This paper introduces a novel approach for learning generalities across graphs via task-trees, which unify node-, edge-, and graph-level tasks by introducing virtual task nodes. The theoretical analysis demonstrates the stability, transferability, and generalization properties of task-trees. Empirically, the p... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. We are encouraged by the positive recognition of our contributions, including the unified task-tree representation, theoretical grounding, and strong empirical performance across diverse settings. In the following, we ad... | null | null | null | null | null | null |
Wait-Less Offline Tuning and Re-solving for Online Decision Making | Accept (poster) | Summary: This paper introduces a hybrid algorithm for Online Linear Programming (OLP) that combines LP-based and first-order methods. By periodically re-solving LPs at frequency \( f \) and using first-order updates in between, the method achieves a regret bound of $\mathcal{O}(\log(T/f) + \sqrt{f}) $, balancing comput... | Rebuttal 1:
Rebuttal: **Response to Reviewer QGM5**
We appreciate your valuable feedback.
**Claims And Evidence**
1. Claims on the assumptions (non-degeneracy)
Thank you for pointing this out. We will add a more detailed discussion to clarify the assumptions in the paper. To achieve $\mathcal{O}(\sqrt{T})$ regre... | Summary: This paper studies online linear programming (OLP) under stochastic inputs. Algorithmic approaches to this problem can be broadly categorized into two types: (i) the LP-based approach, which repeatedly solves an LP using the entire history of observations and decisions, and (ii) the first-order approach, which... | Rebuttal 1:
Rebuttal: **Response to Reviewer GkFi**
Thank you for your positive evaluation of our paper!
**Experimental Designs Or Analyses**
1. Computational efficiency.
We kindly refer the reviewer to Table 3 (Page 7 top) for a running time comparison between different methods, and in the revision we'll add a... | Summary: This paper studies online linear programming and proposes an algorithm based on switching between a first-order method and a linear programming method proposed in previous literature to achieve both better computational efficiency and smaller regret by properly choosing the switching frequency f. There are als... | Rebuttal 1:
Rebuttal: **Response to Reviewer LeBH**
Thank you for the valuable feedback on our paper.
**Other Strengths And Weaknesses**
1. Unknown horizon $T$
This is a very good point. To our knowledge, in the OLP setting, an unknown horizon length $T$ can make the problem much more complicated: it is shown in... | null | null | null | null | null | null | null | null |
CoSER: Coordinating LLM-Based Persona Simulation of Established Roles | Accept (poster) | Summary: The paper introduces CoSER, a dataset and framework designed to enhance Role-Playing Language Agents (RPLAs) by simulating established characters using LLMs. CoSER provides a high-quality dataset containing 17,966 characters from 771 books, featuring authentic dialogues, character experiences, and internal tho... | Rebuttal 1:
Rebuttal: Thanks for your valuable feedback. We have responded to all your comments and questions in detail, and will revise the paper to:
1. Compare authentic v.s. LLM-generated role-playing data with experiments.
2. Experiment with Deepseek V3 and R1 as judges to avoid self-preference bias.
3. Include h... | Summary: The paper introduces CoSER, a framework and accompanying dataset for training and evaluating large language models (LLMs) for role-playing fictional literary characters. To this end, the authors create a dataset derived from authentic dialogues and character contexts across a large amount of 771 books and ~18,... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and valuable comments. We have responded to them in detail, and will revise the paper to:
1. Add more related work on persona-based LLMs.
2. Include human evaluation to further validate our models and evaluation methods.
Please find the details bel... | Summary: This paper introduces a large-scale role-playing dataset Coser. The Coser dataset is extracted from 700 renowned books featuring 18k characters. The authors build Coser with the goal of leveraging this dataset as a high-quality resource for given-circumstance acting in large language models. Two fine-tuned mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and advice. We have responded to your comments and questions in detail. We hope our responses will help you better recognize the details of our work and findings.
Please find the details below:
> Q1: Generalizability and Broader implications o... | null | null | null | null | null | null | null | null |
When Can Proxies Improve the Sample Complexity of Preference Learning? | Accept (poster) | Summary: This paper discusses the sample complexity of optimizing LLMs with proxy rewards to improve the true policy. The authors give sufficient conditions, under which the proxy data is guaranteed to improve the sample complexity of learning the true policy. In general, I think this is an important topic as we are us... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our work addresses an important problem and for asking the interesting questions. We answer your questions below.
**Questions For Authors:**
**1. I understand this is a paper focusing on theories. But I am curious about the parameterization part in section 4.3. H... | Summary: The paper studies the problem of reward hacking — eg training an agent when one only has access to proxy reward (and can maximize it) during training, but it leads to the true/gold reward going down. Concretely, the paper studies the setup where one has access to abundant proxy labels, but only few true/gold l... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. We hope to address your main concerns below.
**…says “distinct prompts”, but in the stated condition 1, we do not enforce x1≠x2…**
Thanks for pointing this out, note that we have also relaxed the conditions 1 and 4:
Condition 1 can be relaxed from iff (i.... | Summary: The paper investigates how leveraging abundant proxy data---feedback from less (proxy) expert sources---can pre-train a model to obtain a low-dimensional representation, which then serves as a warm-start for learning a true policy from limited high-quality (true) expert data. Here, the true policy refers to th... | Rebuttal 1:
Rebuttal: Thank you for your encouraging comments and insightful questions. We hope to address your main concerns below.
**Weakness 1 & 2: The derived bounds appear to be vacuous at first glance…**
This is a good question. Note that in the number of samples bound in Theorem 5 and 6, there are three consta... | Summary: The paper provides a theoretical framework for aligning policies under two different preference models—one “proxy” preference (e.g., from a reward model) and one “true” preference (e.g., from actual human judgments). The main contribution is a set of conditions under which the optimal policy derived from the p... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments, we hope to address your main concerns below.
**Strong conditions; Hard-to-verify assumptions:**
First we point out that while our conditions may be stringent, there exist a lot of ground truth -> proxy shifts that do meet our criteria; our results show tha... | null | null | null | null | null | null |
Forest-of-Thought: Scaling Test-Time Compute for Enhancing LLM Reasoning | Accept (poster) | Summary: The paper introduces the Forest-of-Thought framework, a novel approach to enhance LLM reasoning by integrating multiple reasoning trees. The key innovations include sparse activation strategies, dynamic self-correction, and consensus-guided decision-making. Experiments suggest that FoT improves accuracy and ef... | Rebuttal 1:
Rebuttal: We appreciate the reviewers' careful reading and valuable comments. We believe these constructive feedback will help improve the paper. Below are responses to some specific comments.
---
**Q1: Weaknesses1: Detailed analysis of cost-accuracy trade-off compared to ToT.**
**A1:** Thank you for yo... | Summary: This paper presented the Forest-of-Thought (FoT) to extend the former CoT and ToT via integrating multiple reasoning trees and making majority vote, and to avoid the computation complexity of building numerous trees and further improve the performance, the paper employed a set of sparse activation and self-cor... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments and valuable suggestions. We appreciate the time spent evaluating our work, and we have carefully considered each of your points. Below are our responses to the concerns and suggestions raised.
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**Question 1 & Suggestion 2: FoT's performance varies with ... | Summary: This paper proposes Forest-of-Thought (FoT), a new reasoning framework designed to enhance reasoning ability during test time by combining multiple reasoning trees. This method introduces three strategies (i.e., sparse activation, dynamic self-correction, and consensus-guided decision-making) to enhance both p... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful and comprehensive feedback provided by our reviewers. We will now address the suggestions in detail.
**Q1: Question 1: Data augmentation for the Game of 24 and supplementary experiments on MATH500 and GSM8K.**
**A1:** The Game of 24 input consists of four n... | Summary: This paper presents Forest-of-Thought, a reasoning framework designed to enhance the reasoning of LLMs. FoT integrates multiple reasoning trees to leverage collective decision-making for solving complex logical problems. It employs sparse activation strategies to select the most relevant reasoning paths, impro... | Rebuttal 1:
Rebuttal: We are grateful for your time and thoughtful suggestions, which will guide us in improving both the framework and its implementation in future iterations. Below please find the responses to some specific comments.
---
**Weakness 1: Computation Efficiency Compared to Single-Path Reasoning.**
**A1... | null | null | null | null | null | null |
MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache | Reject | Summary: The authors focus on the problem of high latency in MoE inference on personal machines with limited GPU memory. They observe that most existing offloading-based inference systems fail to effectively utilize the sparsity of expert activations during inference, leading to poor cache performance and high latency.... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We would like to address your comments and questions as follows.
# Questions For Authors:
## 1. The authors propose a method to predict expert activations during decoding based on historical activation traces. Could you please give more details on how to man... | Summary: Mixture-of-Experts (MoE)-based Large Language Models have recently exhibited strong performance across a wide range of tasks. However, their substantial model size poses significant challenges for deployment in resource-constrained environments. Expert-based caching has emerged as a promising approach to allev... | Rebuttal 1:
Rebuttal: Thank you for the detailed feedback—we’ll revise the figures and writing for clarity, and address key concerns below.
# Questions For Authors:
## 1. Clarification to vLLM MoE Offloading implementation to ensure apple-to-apple comparison.
Thanks for raising this point. We include vLLM as it is... | Summary: The authors introduce MoE-Infinity, a system targeting efficient inference for MoE models with a batch size of one, designed for personal machines with limited GPU memory. MoE-Infinity dynamically traces sparse activation patterns of experts during inference and optimizes caching and prefetching decisions to m... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. We address your concerns and comments as follows.
# Questions for Authors:
No questions
# Others:
## 1. **Accessibility for General ML Audience:** The paper can be difficult to follow for a general ML audience due to its engineering-heavy f... | Summary: The paper introduces MoE-Infinity, an inference system optimized for Mixture-of-Experts (MoE) models on personal machines with limited GPU memory. Driven by their finding of single batch inference exhibiting a high degree of activation sparsity, the authors design a sparsity-aware expert cache, which can trace... | Rebuttal 1:
Rebuttal: Thank you for the helpful suggestions. Below, we address the remaining concerns.
# Questions:
## 1. 75% of experts are never activated?
We clarify this does not mean 75% of experts are never activated. The 25% activation rate is inherent to Mixtral’s design, activating the top 2 out of 8 experts ... | null | null | null | null | null | null |
A Selective Learning Method for Temporal Graph Continual Learning | Accept (poster) | Summary: This paper introduces Temporal Graph Continual Learning (TGCL), a novel problem setting that tackles the challenge of updating models on dynamic temporal graphs, where both new-class data emerge and old-class data evolve over time. To address this, the authors propose Learning Towards the Future (LTF), a selec... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's efforts in reviewing our paper. We thank the reviewer for recognizing our comprehensive coverage of the problem and method, detailed analysis and experiments, and clear presentation. Our responses to the comments on motivation, problem setting, and experiment a... | Summary: The paper defines Temporal Graph Continual Learning (TGCL) as the problem of node classification on dynamically evolving graphs, where new unseen classes emerge, and old-class data distributions shift over time. Existing methods struggle with catastrophic forgetting when updating models in such settings, as th... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's efforts in reviewing our paper. We thank the reviewer for recognizing our novelty, sound theory, and good presentation. Our responses to the comments on motivation and more comprehensive experiments are presented below:
**Q1**: Clarify the motivation, especial... | Summary: In this paper, the authors propose the novel problem of temporal graph continual learning where new classes can emerge in a temporal graph. To solve this task, the authors proposed the learning towards the future framework and derive theoretical insight into the upper bound of error due to graph subsampling. T... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's efforts in reviewing our paper. We thank the reviewer for recognizing our novel learning paradigm and the good correspondence between theory and experiments. Our responses to the valuable feedback are presented below:
**Q1**: Experiments on datasets with a longer dura... | Summary: This paper identifies the challenge of effectively and efficiently updating newly introduced classed in temporal graph node classification. To address this, the authors propose a novel optimization objective that dynamically integrates the loss distribution of both old and new categories over time. Additionall... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's efforts in reviewing our paper. We thank the reviewer for recognizing our novel problem, well-developed method, and rigorous experiments. Our responses to the valuable feedback are presented below:
**W1**: Given various efficiency improvements in the proposed ... | null | null | null | null | null | null |
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing | Accept (poster) | Summary: This paper proposes a synthetic framework to investigate knowledge editing in Transformer-based language models. The authors learn a model from structured knowledge out of a graph featuring multiple cyclic orders of entities and relations of facts. By evaluating representation changes following targeted edits ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our empirical analysis and mechanism of representation shattering. Please see our detailed responses to specific comments below.
---
> **#1: Multi-Step Editing**
Thank you for this suggestion! We note we already provide an analysis of multi-step and layerw... | Summary: This paper explores the empirical finding that knowledge editing can degrade the general capabilities of large language models. The authors perform a synthetic in the setting of structured knowledge graphs (i.e. knowledge graphs with a cyclic or tree structure). They make the finding that the latent space acti... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our experiments and analysis of representation shattering. Please see our responses below.
---
> **#1: Generality and Tree-Structured KGs**
We acknowledge that our tree-based experiment in App. G.3 is preliminary, but believe further exploration of tree-sh... | Summary: This paper explores the impact of Knowledge Editing (KE) on Transformer models and introduces the concept of Representation Shattering. The authors argue that modifying specific facts in the model will destroy its broader internal knowledge structure, leading to reduced fact recall and reasoning capabilities. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our experiments and analysis of representation shattering. Please see our responses below.
---
> **#1: Scope of Models and Methods Tested**
We emphasize our evaluation already spans multiple architectures and scales: small Transformer models trained from s... | Summary: This paper proposes a fundamental principle to understand why existing Knowledge Editing (KEs) methods often introduce unexpected cascading effects on knowledge not tampered with during edition and cause the edited LLMs to yield inconsistent reasoning results. Specifically, the authors argue that, especially f... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our experiments and analysis of representation shattering. Please see our responses below.
---
> **#1: $R(D_*)$ and Permutation Invariance**
Great question! We intentionally made our distortion metric $R(D_*)$ to not be permutation-invariant, i.e., $R(D_*) ... | null | null | null | null | null | null |
Generative Data Mining with Longtail-Guided Diffusion | Accept (poster) | Summary: The paper proposes Longtail Guidance (LTG), a method for generating rare or difficult training examples that a predictive model struggles with. The authors propose using an epistemic head to estimate uncertainty and use its signals guide a diffusion model to synthesize hard or rare data without retraining eith... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insights. We are grateful for two solid accepts [Yasy, Qmgq] and believe that we can fully address the concerns of the weak reject [iGg6]. We respond as best as we are able to the extremely brief review of [srKG].
We are pleased to hear from reviewers tha... | Summary: The paper proposes a diffusion model guidance technique (LTG) that generates synthetic training data tailored to the specific long-tail of a deployed predictive model. It introduces a differentiable module for estimating epistemic uncertainty that helps identify rare or hard examples without altering model wei... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insights. We are grateful for two solid accepts [Yasy, Qmgq] and believe that we can fully address the concerns of the weak reject [iGg6]. We respond as best as we are able to the extremely brief review of [srKG].
We are pleased to hear from reviewers tha... | Summary: This paper proposes a novel approach to generate long-tail data using diffusion models. The authors introduce an epistemic head and a long-tail guidance mechanism, enabling the model to detect and generate long-tail data effectively. Experimental results demonstrate that the proposed Longtail-Guided Diffusion ... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insights. We are grateful for two solid accepts [Yasy, Qmgq] and believe that we can fully address the concerns of the weak reject [iGg6]. We respond as best as we are able to the extremely brief review of [srKG].
We are pleased to hear from reviewers tha... | Summary: This paper proposes a proactive long-tail discovery process that helps the model learn rare or hard concepts.
Specifically, the authors develop model-based long-tail signals and use these signals to generate additional training data from latent diffusion models.
Claims And Evidence: Producing rare or hard con... | Rebuttal 1:
Rebuttal: We thank the reviewers for their time and insights. We are grateful for two solid accepts [Yasy, Qmgq] and believe that we can fully address the concerns of the weak reject [iGg6]. We respond as best as we are able to the extremely brief review of [srKG].
We are pleased to hear from reviewers tha... | null | null | null | null | null | null |
Off-Policy Evaluation of Ranking Policies for Large Action Spaces via Embeddings and User Behavior Assumption | Reject | Summary: This paper addresses off-policy evaluation in ranking contexts when the action space is large, driven by both the number of unique items and the ranking length. Existing estimators often suffer from excessive variance or high bias. The authors propose a Generalized Marginalized IPS (GMIPS) framework that relie... | Rebuttal 1:
Rebuttal: We appreciate your review of our proposal paper. Below, we would like to address questions and weaknesses you have raised.
>”How sensitive is GMIPS to suboptimal or incorrectly learned embeddings? Any early stopping or representation learning guidelines?”
Thank you for your insightful questions.... | Summary: This paper studies off-policy evaluation (OPE) for the ranking problem. The key challenges in this setting are the length of ranking and the number of actions that may be chosen for each position. To deal with these difficulties, there are two distinct existing works. One is introducing some user behavior assu... | Rebuttal 1:
Rebuttal: We appreciate your review of our proposal paper. Below, we would like to address questions and weaknesses you have raised.
>”Are there further advantages of the proposed method than variance reduction enjoys (variance reduction of AIPS/GIPS) + (variance reduction of using action embeddings in AIP... | Summary: The paper studies off-policy evaluation for ranking policies. A key challenge lies in the large action spaces, which makes OPE difficult as the distributional shift between target and behavior policies become more pronounced in these settings. To address this challenge, the author(s) proposed to employ actions... | Rebuttal 1:
Rebuttal: We appreciate your review of our proposal paper. Below, we would like to address questions and weaknesses you have raised.
>"Would you please give some specific examples of action embeddings if they are unknown?”
Thank you for your insightful question. If the action embeddings $\boldsymbol{e}$ a... | Summary: - The paper addresses the problem of off-policy evaluation (OPE) in environments with large ranking action spaces. A key challenge in this area is the high variance associated with existing estimators.
- To tackle this, the authors introduce two assumptions:
- No Direct Effect on Rankings
- User Behavior ... | Rebuttal 1:
Rebuttal: We appreciate your review of our proposal paper. Below, we would like to address weaknesses you have raised.
>”Based on the experimental results, such as those presented in Figure 2, the MIIPS estimator does not show significant improvements over the snIIPS estimator compared to other variants.”
... | null | null | null | null | null | null |
Safety Reasoning with Guidelines | Accept (poster) | Summary: This paper investigate how to defend against OOD jailbreak attacks. Compared with existing work, this paper claims that the failure of refusal training in defending against jailbreak attacks is not because the model possesses sufficient safety-related latent knowledge, but fails to consistently elicit this kno... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our work, as well as for recognizing our contributions!
---
### Response to Weakness 1:
1. Apart from o1 system card and Deliberative alignment paper from OpenAI, no prior work from academic community demonstrates reasoning could enhance safety perform... | Summary: The paper aims to improve safety alignment by leveraging reasoning with guidelines in rejection training (RT). The main contributions are:
1. shows through Best-of-N evaluations that RT models have sufficient safety-related latent knowledge, which is not fully utilized when trained with direct refusal.
2. Prop... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our work, as well as for recognizing our contributions!
------
### Response to Weakness and Comments:
Thanks for the reminder. We will reformat the figure layout, including training and evaluation steps, to improve the readability of our pipeline. W... | Summary: This work focuses on improving the safety alignment of language models by leveraging their reasoning abilities. The authors highlight the limitations of direct refusal training, which can lead to superficial shortcuts and non-robust representation mappings. To address these issues, they propose **Safety Reason... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our work.
---
### Response to Claims and Methods:
1. Thanks for your comments. We evaluate over-refusal using XSTest dataset [5], as shown below. Our method outperforms LAT and GPT-4o, achieving 92%, slightly behind LLaMA3-8B-Instruct (baseline). RR pe... | Summary: This paper examines the limitations of Refusal Training (RT) in improving the safety of large language models (LLMs), particularly its failure to generalize against out-of-distribution (OOD) jailbreaking attacks. While many approaches focus on enhancing refusal strategies, the authors argue that RT models alre... | Rebuttal 1:
Rebuttal: Thanks for your time and effort in reviewing our work, as well as for recognizing our contributions!
---
### Response to Experimental Designs:
1. Thanks for your suggestion. We evaluated over-refusal using XSTest dataset [4], as shown below. Our method outperforms LAT and GPT-4o, achieving 92%, s... | Summary: The paper investigates the limitations of RT in safety alignment for LLM and proposes Safety Reasoning with Guidelines (SRG), to enhance OOD generalization against jailbreaking attacks.
The authors demonstrate that RT models relies on simple pattern matching thus fail to generalize against OOD attacks.
The au... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our work.
---
### Response to Claim part:
1. We use 'complete pipeline' to indicate we offer a thorough pipeline to train safety reasoning models, which include three parts: 1) synthesizing reasoning supervision w.r.t guidelines (C); 2) rejection sa... | null | null | null | null |
Minimum Width for Universal Approximation using Squashable Activation Functions | Accept (poster) | Summary: The paper investigates the exact minimum network width required for universal approximation of $L^p$ functions on a cube $[0,1]^{d_x}$ by neural networks utilizing *squashable* activation functions. Squashable functions are defined in this paper as activation functions that, when composed alternatively with af... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and thoughtful feedback. We answer the reviewer’s question below.
> In practice, both and norms are too weak because derivatives are not guaranteed to converge. For example, can we state the universality in a Sobolev space?
We thank the review... | Summary: The paper under review proves universal approximation of neural networks with a fixed squashable activation function and minimal width equal to the maximum of input and output dimension on compact sets with respect to the L^p-norm, hence any L^p-function with values in R^{d_y} can be approximated in this norm... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation and thoughtful feedback. We address all comments of the reviewer below.
> It does not occur to me what is the sense of the first assertion in lemma 7. Isn't that obvious?
As the reviewer pointed out, $w_{\sigma}\ge\max\\{d_x,d_y\\}$ is a trivia... | Summary: This paper considers a class of activation functions and provides the minimum width required for the corresponding neural network to have the universal approximation property.
Claims And Evidence: I think the conclusion of this paper is reliable.
Methods And Evaluation Criteria: I think the method in this pa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their time and effort to provide valuable comments. We address all comments of the reviewer below.
> While previous papers have not exhaustively covered all activation functions, it is straightforward to deduce results for many activation functions from existing fin... | Summary: The paper extends previously established result that two neurons per layer are sufficient for universal function approximation in an unbounded depth neural network; the extension establishes the known result for a wider family of activation functions, the so called "squashable" functions, which includes sigmoi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for their positive evaluation and valuable comments. We address the reviewer’s concern and clarify our contribution below.
> The result itself is an incremental contribution, extending known results that already covered most popular activation functions, to wider set of... | null | null | null | null | null | null |
Predictive Performance of Deep Quantum Data Re-uploading Models | Accept (poster) | Summary: This paper provides further theoretical insights into the reuploading approach of parameterized quantum circuits. They authors claim the divergence (and predictive error) are worse with increasing numbers of layers. The has implications for near term devices, since the number of qubits is much less than the di... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's thorough and constructive feedback, which has helped us improve the clarity and quality of our manuscript. We address each point below.
## figure 4 it looks like the pre-training is missing/overlapping and is hard to identify.
We thank you for pointing out th... | Summary: The paper investigates the effectiveness of quantum machine learning models that use data re-uploading circuits.
These models have gained attention for their expressivity and trainability, but their ability to make accurate predictions on unseen data remains under-explored.
The study highlights a limitation in... | Rebuttal 1:
Rebuttal: We thank you for the thorough and insightful comments, which helped us improve the clarity and depth of our manuscript. Below we elaborate on the differences between our work and (Li et al. 2022) in terms of overall framework and proof techniques.
## Framework for analyzing prediction error
Rega... | Summary: The authors examine the predictive performance of data re-uploading models, a class of variational quantum circuits which has attracted significant attention in recent years. They prove that under certain theoretical assumptions about the data generating distribution, these models' have the property that as on... | Rebuttal 1:
Rebuttal: We thank you for the insightful questions and valuable feedback. Our responses to your questions are as below.
## 1. The results suggest that any data re-uploading model (in the regime studied by the authors, and with gaussian inputs etc) will be roughly speaking "mean zero". Could one not add a ... | null | null | null | null | null | null | null | null |
DyPolySeg: Taylor Series-Inspired Dynamic Polynomial Fitting Network for Few-shot Point Cloud Semantic Segmentation | Accept (poster) | Summary: The author proposed an interesting method to solve the problem of semantic segmentation of few-shot point clouds. Specifically, the author proposed a local feature aggregation convolution inspired by Taylor series, and built the entire model backbone on this basis. To specifically solve the problem of semantic... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our method and detailed review. We have responded to your concerns in detail and hope to address all your questions.
**Concern 1:** Although the method has achieved certain advancement, I think it is also necessary to think about the complexity of the model, but ... | Summary: The authors propose a novel framework for semantic segmentation of few-shot point clouds, called DyPolySeg. This framework consists of two parts. The first part is composed of an encoder and a decoder for representation learning of point clouds. In the encoder part, the authors propose a novel DyPolyConv. The ... | Rebuttal 1:
Rebuttal: Thank you for the reviewers' positive comments on our manuscript and their valuable suggestions. We have responded to all your questions in detail, as follows:
**Concern 1:** Although the method proposed by the author achieves the best performance, I would like to know that the author claims to h... | Summary: The paper argues that few-shot point cloud semantic segmentation models are constrained by their pretraining models and introduces a pre-training-free Dynamic Polynomial Fitting network. The network comprises DyPolyConv for local feature extraction and the Mamba Block for global feature extraction. Additionall... | Rebuttal 1:
Rebuttal: Thank you to the reviewer for your time and suggestions, and to the other reviewers for their recognition of our paper. We have carefully addressed your concerns. The specific responses are shown below, and we hope we have resolved your questions.
**Concern 1:** The Method section lacks clarity, ... | Summary: This paper points out that there are three main limitations in the existing methods. First, the methods based on pre-training have domain transfer and increase training time. Second, the current method mainly relies on DGCNN as the backbone, which affects the modeling of several structures of the point cloud. ... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewers for their time in reviewing our manuscript and providing constructive comments. We have carefully addressed your questions below.
**Concern 1:** Although this article mentions the improvement of computing efficiency, the author does not seem to p... | null | null | null | null | null | null |
PAC Learning with Improvements | Accept (poster) | Summary: The paper studies a scenario in which "agents" adapt in response to the classifier deployed by the learner. Within the PAC framework, the authors derive an upper bound for certain standard hypothesis classes.
Claims And Evidence: Yes, all the theoretical claims are justified by either proof or a sketch of it.... | Rebuttal 1:
Rebuttal: >“why should the hypothesis be penalized at point despite being correct there and offering at least 1 valid improvement option?”
Thank you for your question.
Let's consider a motivating scenario: suppose that in order to do well in your research group, an applicant needs to (a) know basic conc... | Summary: This paper continues the line of work on strategic classification and related settings. The authors consider a binary PAC learning setting where negatively predicted points $x$ can change their prediction slightly (defined by some set $\Delta(x)$); the expected loss is then evaluated on this potentially altere... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and very interesting questions which inspire directions for promising future research.
>“The threshold/half space examples have vague similarities with learning with margin (or perhaps a one-sided version of it). Is there any relationship betw... | Summary: This paper introduces a novel framework called PAC learning with improvements. The manuscript outlines the setting as one in which data points correspond to individuals that can improvement themselves. A key feature of this framework is that the improvement is real and as such, a classifier can be correct on a... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our work, and for the insightful questions and suggestions.
>Relationship between FP weight and imbalance
At this moment, we cannot confirm a specific relationship. However, the choice of false positive and false negative weights required to train a positi... | Summary: The paper proposes a variant of PAC learning where the data points are conceived as agents that can move around by a small amount and thus if they are close to the decision boundary, they can be classified as desired (typically positive; e.g., for receiving a loan after some small "effort" in order to move the... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. Below are responses to the questions they raised.
>Terminology - “hypothesis class”[H] vs. “concept class” [C]
We indeed make the common assumption of implicitly taking H=C.
While earlier papers in the field considered the case whe... | null | null | null | null | null | null |
Quantum Speedups in Regret Analysis of Infinite Horizon Average-Reward Markov Decision Processes | Accept (poster) | Summary: This paper leverages the result in Cornelissen et al. for quantum acceleration of the mean estimation problem for $d$ dimensional random variable when the number $n$ of quantum experiments is larger than the dimension $d$.
Leveraging this fact the authors can reproduce the standard UCRL proof for Jaksch et al.... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful comments and valuable suggestions. Below we carefully address each of your concerns:
## **Bound on epochs $E$ in Equation (84)**
We appreciate your careful review. Indeed, your observation is correct that the bound on the number of epochs $E$ as $E \leq p... | Summary: Summary
The authors study the problem of regret analysis—specifically, finding the policy that minimizes regret—when navigating Markov Decision Processes (MDPs) with simultaneous quantum and classical access to the environment. They propose a concrete quantum protocol based on the quantum mean estimation algor... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback and valuable suggestions. We address each point carefully and explicitly below:
## **Comparison with [1]**
We clarify that the problem studied in [1] is fundamentally different from our setting:
- **Episodic vs. Infinite Horizon:**
Reference... | Summary: The paper addresses regret minimization in infinite-horizon MDPs with average reward optimality criteria. Specifically, the aim of the paper is to show a quantum speedup in the regret by employing quantum mean estimators. The paper presents an optimistic algorithm, a variation of the classical UCRL adapted to ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful review. We address each point carefully below, hoping to clarify our contributions clearly.
## **Writing style intended for ML community**
Indeed, our intended audience is the ML/RL community, which may not be deeply familiar with quantum comp... | null | null | null | null | null | null | null | null |
Learning from others' mistakes: Finetuning machine translation models with span-level error annotations | Accept (poster) | Summary: This paper proposes training with annotations (TWA), a method for finetuning machine translation models using existing span-level error annotations. Unlike traditional approaches that rely on sequence-level feedback, TWA directly leverages fine-grained annotations for model training. The authors evaluate TWA o... | Rebuttal 1:
Rebuttal: Thanks for your review. Responding to your concerns below:
[Lack of human evaluation]:
While we agree that human evaluation would strengthen the results, we do show improvements over multiple automated quality metrics, including a held out metric that was not used during any part of model select... | Summary: This paper focuses on improving machine translation models by leveraging span-level error annotations. It proposes a new algorithm called Training with Annotations (TWA). The core idea of the TWA is applying a weighted span-level unlikelihood loss to error spans to encourage the model to learn which tokens to ... | Rebuttal 1:
Rebuttal: Clarifying experimental details and questions:
1. Submissions are the machine translations from models entered into the WMT competition.
2. We pre-trained two base models using a different bilingual dataset for each.
3. The other MT systems are the specific models entered into the WMT competition ... | Summary: This paper explored a new approach to fine-tune machine translation models by utilizing fine-grained span-level annotations for further quality improvement. Previous work mostly focus on sequence-level annotations. while this work takes advantage of more fine-grained span-level annotations. The authors careful... | Rebuttal 1:
Rebuttal: Thank you for your review! Please see our response below:
[sacreBLEU ...]:
Good question. To compare the effectiveness of TWA in using targeted negative information we compare the BLEU score of the model after TWA on submissions and references versus the model after DPO on submissions and refere... | Summary: This paper develops a simple finetuning algorithm, called Training with Annotations (TWA), to directly train machine translation models on this annotated data. TWA utilizes targeted span-level error information while also flexibly learning what to penalize within a span. Moreover, TWA considers the overall tra... | Rebuttal 1:
Rebuttal: Thank you for your positive review! We appreciate your compliment about the novelty of the idea and the feedback that the paper is well-written. We agree with how you related our work to the broader literature as well. | null | null | null | null | null | null |
Constrain Alignment with Sparse Autoencoders | Accept (poster) | Summary: The author proposes Feature-level Constrained Preference Optimization (FPO). FPO uses a SimPO objective plus a regularizer that compares features in a lower-dimensional space, rather than token probabilities in the high-dimensional vocabulary space. These features are obtained by a sparse autoencoder. In this ... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's suggestions. We note that the reviewer has raised three valuable concerns, and we would like to address them.
**Q1: To me, a shortcoming of the paper .... doesn't seem to be relevant to this regularization.**
We acknowledge this gap in our original submissio... | Summary: This paper proposed a DPO variant that is both good performing and computationally efficient. They replace expensive per token KL divergence regularization w/ an SAE-based regularization -- hinging the efficiency on the SAE sparsity.
Claims And Evidence: Somewhat -- the theory part connecting FPO (theirs) los... | Rebuttal 1:
Rebuttal: **Q1: Only 2 models are used of the same family.**
We apologize for the lack of evaluation across other model families. During our experiments with FPO, the Gemma model series was the only one with a relatively complete set of SAE models. Consequently, we limited our training and testing to the Ge... | Summary: The paper proposes Feature-level constrained Preference Optimization (FPO), a novel method designed to improve the alignment of LLMs. FPO utilizes sparse features activated in a trained sparse autoencoder and employs feature-level offline reference to maintain the quality of sequential KL divergence. Experimen... | Rebuttal 1:
Rebuttal: **Q1: The paper doesn’t have a related work section and doesn’t offer a sufficiently comprehensive overview of key prior work in LLM alignments**
We are sorry for missing the related work section due to the page limit. Considering our work lie on the intersection of alignment and mechanistic inte... | Summary: This work proposes a feature-level direct preference optimization algorithm, FPO, for LLM preference learning. Specifically, it revised the token-level KL reguralization of TDPO into the feature level KL regularization where features are obtained from a pre-trained SAE.
The reference features and tokens are b... | Rebuttal 1:
Rebuttal: **Q1: Why the title is Accurately? The key advantages of this work are its efficiency and reduced memory usage.**
We appreciate the reviewer’s question regarding the title’s use of “Accurately” and their observation that the work’s key advantages seem to lie in efficiency and reduced memory usage... | null | null | null | null | null | null |
Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models | Accept (poster) | Summary: This paper introduced a new dataset, named **V**isual **G**raph **A**rena (**VGA**), specifically designed to evaluate and enhance deep models for visual graph analysis. VGA comprises six distinct tasks: Easy Isomorphism, Hard Isomorphism, Hamiltonian Path, Shortest Path, Hamiltonian Cycle, and Biggest Chordle... | Rebuttal 1:
Rebuttal: Thank you for thoughtful comments and the time you spent for the detailed review. We hope to address your concerns below:
**More Baselines**
We acknowledge your suggestion to include more baseline models. We agree this would strengthen our analysis and have already begun running these additional... | Summary: The paper proposes a new benchmark to evaluate whether visual models can understand underlying concepts with different visual appearances. The benchmark contains generated graphs with three layouts: Random, Kamada-Kawai, and Planar. The authors then train the visual model on trained on the Kawai layout, then a... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback and positive evaluation of our paper. We're happy that you found our benchmark sound and valuable for evaluating visual models' ability to understand concepts across different visual representations. Below we try to address your concerns.
**Additional dat... | Summary: This paper investigates multimodal models' ‘conceptualization’—the ability to recognize and reason about the same concept despite variations in visual form, a basic ability of human reasoning. They introduce the Visual Graph Arena (VGA), a dataset featuring
six graph-based tasks designed to evaluate and improv... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thoughtful and detailed feedback. We appreciate the positive reception to the core idea of VGA. Below, we address each of the points raised:
**3D format**
You raise an interesting point about testing graphs in 3D format. Our current focus was on testing visual co... | Summary: This paper presents visual graph arena (VGA), a multimodal dataset designed to evaluate and improve AI systems’ capacity for visual abstraction.
Although being straightforward for humans, the authors find that VGA is very challenging for current MLLMs: they totally fail on some of the tasks in VGA and show li... | Rebuttal 1:
Rebuttal: We thank you for your positive assessment of our work and for helpful comments. Below, we address your questions and suggestions.
**Distinction from Related Work**
The reviewer asked how VGA differs from the cited papers on visual reasoning. While all these works explore aspects of visual abstrac... | null | null | null | null | null | null |
Learning Soft Sparse Shapes for Efficient Time-Series Classification | Accept (spotlight poster) | Summary: The paper focuses on the univariate time series classification problem. It proposes a method called Soft Sparse Shapes (SoftShape) model. The model has two major components: 1) a soft shape sparsification module, and 2) a soft shape learning block. The soft shape sparsification module categorizes the input tok... | Rebuttal 1:
Rebuttal: **W1: The claim is mostly supported by the ablation study. It would be better if the authors provided examples through theoretical analysis or demonstrated it with simple toy data.**
**A**: SoftShape include:
1) Soft Shape Sparsification
- To justify its design, we provide toy real-world data (... | Summary: This paper presents Soft Sparse Shapes (SoftShape) for efficient time-series classification. It introduces soft shape sparsification to improve training efficiency by converting subsequences into soft representations. The model further enhances performance by employing a mixture of experts for intra-shape and ... | Rebuttal 1:
Rebuttal: **W1 & Q2: The SoftShape model is reliance on fixed-length patch partitions for dividing the input time series, which may result in the loss of important discriminative information. Exploring the possibility of variable patch lengths might enhance the model’s flexibility and potentially improve it... | Summary: This paper presents SoftShape, a learning-based soft sparse shapes model for time series classification, designed to enhance model interpretability. Specifically, SoftShape introduces the soft shape sparsification, replacing hard shapelets with soft shapelets to improve training efficiency. Moreover, SoftShape... | Rebuttal 1:
Rebuttal: **W1: The authors do not provide an in-depth analysis of the networks used for class-specific experts and the shared expert.**
**A**: Please refer to the answers in Q2 and Q3.
---
**W2 & Q4: The authors do not discuss the rationale for using warm-up training.**
**A**: During the early trainin... | Summary: This paper focus on time-series classification using shapelets. It introduce an attention based sparsification mechanism that merges the less discriminative subsequences into a single shape based their learned attention scores. A Mixture of Experts (MoE) architecture is used to learn intra-shape patterns an... | Rebuttal 1:
Rebuttal: **W1: MultiRocket-Hydra (MR-H) should be added to this comparison as a computationally efficient baseline.**
**A**: MR-H is a combination of the Hydra [1] and MultiRocket [2] algorithm.
- Hydra uses randomly initialized convolutional kernels, grouped into $g$ groups per dilation with $k$ kernel... | null | null | null | null | null | null |
Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization | Accept (poster) | Summary: To build an effective discrete vocabulary for a real-valued sequential input, this paper develops WaveToken, a wavelet-based tokenizer that allows models to learn complex representations directly in the space of time-localized frequencies. The proposed method performs well while using a much smaller vocabulary... | Rebuttal 1:
Rebuttal: Thank you so much for these useful comments and questions that helped improve our paper. Below we provide point-by-point answers to each section in your review.
---
**Additional baselines**
Based on your and Reviewer BBt2’s suggestion, we evaluated TimeMOE and TTM-R2 models. WaveToken clearly ou... | Summary: This paper introduces WaveToken, a wavelet-based tokenization framework designed to enhance foundation models for time series forecasting. The approach leverages the multi-resolution properties of wavelets to transform real-valued time series into compact, expressive token sequences, enabling efficient learnin... | Rebuttal 1:
Rebuttal: Thank you for these useful comments and questions that helped improve our paper. Below we provide point-by-point answers to each section in your review.
---
> The code is not publicly available, hindering replication.
We plan to release a user-friendly research package complete with details on h... | Summary: This paper introduces WaveToken, a wavelet-based tokenization method for time series forecasting. It decomposes time series into wavelet coefficients, which are then used to autoregressively predict future values. The method involves standardizing, decomposing, thresholding, and quantizing the coefficients, an... | Rebuttal 1:
Rebuttal: Thank you for these useful comments and questions that helped improve our paper. Below we provide point-by-point answers to each section in your review.
---
> I would like to know if the proposed method can handle variable-length inputs and outputs with a single model.
The current implementation... | Summary: The manuscript describes a method to tokenize univariate time series data using wavelet transform. The proposed method consists of discrete wavelet transformation followed by a quantization, which generates tokens equal size to the length of input data. Then, the model is trained to forecast the wavelet coeffi... | Rebuttal 1:
Rebuttal: Thank you for your review and comments that have helped us improve our manuscript. Below we provide point-by-point answers to each section in your review.
---
> why not train Chronos with WaveToken?
Chronos itself is based on the T5 architecture (Ansari et al. (2024)) and we use the same model h... | null | null | null | null | null | null |
A Reduction Framework for Distributionally Robust Reinforcement Learning under Average Reward | Accept (poster) | Summary: The paper "Efficient and Scalable Reinforcement Learning for Average Reward under Model Uncertainty" focuses on solving robust reinforcement learning (RL) with the average reward criterion, which is crucial for optimizing long-term performance in uncertain environments. The key challenge is that most robust RL... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time and thoughtful feedback, and we are glad to hear that our work is appreciated.
We conducted additional experiments, at the link: https://anonymous.4open.science/r/ICML-2662-4C1E/README.md
**W1: Scalability is demonstrated using linear function. No th... | Summary: This work builds a framework to aid in the reduction of average reward MDPs to robust discounted reward MDPs. Previous work has shown that as discount factor approaches 1, a policies value function in a robust discount reward MDP approaches the return found in an average reward MDP. This work builds a framewor... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time and thoughtful feedback, and we are glad to hear that our work is appreciated.
We conducted additional experiments, at the link: https://anonymous.4open.science/r/ICML-2662-4C1E/README.md
**W1: More extensive empirical evaluation.**
Since we focus ma... | Summary: This work proposes a reduction-based framework that converts robust average reward optimization into robust discounted reward optimization by selecting an appropriate discount factor. The framework focuses on total variation (TV) and $\chi^2$ divergence and introduces a model-based algorithm with near-optimal ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time and thoughtful feedback, and we are glad to hear that our work is appreciated.
We conducted additional experiments, at the link: https://anonymous.4open.science/r/ICML-2662-4C1E/README.md
**W1: Results for other uncertainty sets**
We first want to h... | Summary: The paper introduces a reduction-based framework for solving robust average Markov Decision Processes (AMDPs) by transforming them into robust discounted MDPs (DMDPs). This framework allows existing methods for discounted reinforcement learning to be applied effectively to the average reward setting, ensuring ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for your time and thoughtful feedback, and we are glad to hear that our work is appreciated.
We conducted additional experiments, at the link: https://anonymous.4open.science/r/ICML-2662-4C1E/README.md
**W1: The estimation of $\mathcal{H}$.**
Since we mainly focu... | null | null | null | null | null | null |
The Global Convergence Time of Stochastic Gradient Descent in Non-Convex Landscapes: Sharp Estimates via Large Deviations | Accept (poster) | Summary: The paper studies the global convergence time of stochastic gradient descent (SGD) in non-convex optimisation landscapes. By employing tools of large deviation theory and randomly perturbed dynamical systems, the authors provide upper and lower bounds on the global convergence time. These bounds are dominated ... | Rebuttal 1:
Rebuttal: Thank you for your time, input, and positive evaluation! We reply to your remarks and questions below:
- **Use of other benchmarks.** Sure thing! We took advantage of the rebuttal period to run numerical experiments on three standard non-convex benchmarks: the three-humped camel benchmark (alread... | Summary: This paper investigates the time required for SGD to attain the global minimum of a general, non-convex loss function. The authors approach this problem using large deviations theory and randomly perturbed dynamical systems and offer a exact characterization of SGD's hitting times with matching upper and lower... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your input and your assessment that "the idea of analyzing the hitting time on the set of minimizers is novel". We reply to your questions and remarks below, and we will of course integrate this discussion in the paper at the first revision opportunity.
- **Practrica... | Summary: This paper answers a hard question in the optimization theory: how long it takes for the SGD to reach the global minimum of a general non-convex loss function. The answer is given in Theorem 1 and Theorem 2: the expected time is exponentially proportional to $E[Q]/\eta$. Later, the author characterizes $E[Q]$,... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time, input, positive evaluation and appreciation! We reply to your remarks and questions below:
- **On the beginning of Section 4.** Yes, we designed this part as a ramp-up to the technical apparatus required to state our results. As you mentionned, this is just... | Summary: This article analyzes global convergence of SGD on non-convex optimization from the large deviation theory in probability. The question of how long does it take SGD to reach the vicinity of a global minimum of a loss f is studied. A tight theoretical estimation about this time is obtained under suitable assump... | Rebuttal 1:
Rebuttal: Dear reviewer,
Thank you for your time and input. We reply to your questions and remarks below, and we will of course update our paper accordingly at the first revision opportunity.
- **The notion of the quasi-potential.** This concept plays a central role in the theory of large deviations as de... | null | null | null | null | null | null |
FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for Scalable Training | Accept (poster) | Summary: This paper presents a novel approach to reduce memory overhead during LLM training by dividing the model parameters into two distinct groups. One group is optimized using Adam-family optimizers, which maintain optimizer states, while the other group is trained with state-free optimization methods such as SGD a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback and respond to their concerns and questions hereafter.
The tables that we will refer to using an apostrophe (e.g., Table 1') can be found at the anonymous link https://anonymous.4open.science/r/frugal-618F/rebuttal.pdf.
>Recent advancements, such... | Summary: This paper focuses on memory-efficient training by using different optimizers on different subspaces of the gradient, and uses different methods for projecting the gradient onto the state-full subspace. They extend AdaLayer to use signSGD instead of SGDM.
Claims And Evidence: The proposed method outperforms e... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's comprehensive feedback. We are glad that they appreciated the theoretical convergence guarantees and strong experimental results. We also answer their questions below. The tables that we will refer to using an apostrophe (e.g., Table 1') can be found at the anonymous l... | Summary: The paper proposes a memory efficient way of combining existing stateful (like Adam) and stateless (like signSGD) optimizers, by running stateful optimizers in a low dimensional space and stateless optimizers in the complementary space. They provide results on pretraining as well as finetuning setup and show t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their detailed comments. We appreciate their commendation of our extensive experimental evaluation and the ablation study we conducted. We address their concerns and questions below.
The tables and proof that we will refer to using an apostrophe (e.g., Tabl... | Summary: The paper introduces FRUGAL, a memory-efficient optimization framework designed for scalable training of large language models (LLMs). The key idea behind FRUGAL is gradient splitting, which enables a mix of stateful optimizers (e.g., AdamW) for a low-dimensional subspace and state-free optimizers (e.g., signS... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback and address their questions below.
The tables and proof that we will refer to using an apostrophe (e.g., Table 1') can be found at the anonymous link https://anonymous.4open.science/r/frugal-618F/rebuttal.pdf.
>Computational Overhead: How does FRUGAL comp... | null | null | null | null | null | null |
Knowledge Swapping via Learning and Unlearning | Accept (poster) | Summary: 1. This paper introduces Knowledge Swapping, a novel task designed to selectively regulate a pretrained model's knowledge by enabling the forgetting of user-specified information while retaining essential knowledge and acquiring new knowledge simultaneously.
2. The authors propose a two-stage training strate... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer ab73 for the valuable comments. Reviewer ab73 notes that our **claims are well-supported by experimental evidence and technical details**, acknowledges that **Tables 1–3 demonstrate superior results** for our proposed method compared to the reverse approach, commends ou... | Summary: This paper introduces a new task called Knowledge Swapping, which aims to regulate the knowledge of a pretrained model by optimizing three objectives: forgetting user-specified knowledge, retaining core pretrained knowledge, and simultaneously learning new knowledge. The authors empirically demonstrate that le... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer tJYm for the insightful and constructive comments. Reviewer tJYm finds that "Knowledge Swapping is a **practical and novel task**", “the experimental design is overall **valid**”, and "learning before forgetting would be a **substantial contribution**". We address the ... | Summary: This paper proposed Knowledge Swapping, a novel task designed to regulate knowledge of a pretrained model selectively. Meanwhile, this paper uncovers that incremental learning progresses from low-level to higher-level semantic features, whereas targeted forgetting begins at high-level semantics and works downw... | Rebuttal 1:
Rebuttal: We thank Reviewer **3jJx** for the valuable comments. Reviewer 3jJx appreciates that "**Knowledge Swapping is a good task**," "is **interesting and novel**," "**well-structured**," represents a "**novel and intriguing concept**," and is "**robust**." Below, we provide detailed responses addressing... | null | null | null | null | null | null | null | null |
Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees | Accept (poster) | Summary: Refer to the abstract.
## update after rebuttal:
Following the discussion I updated my recommendation to weak-reject (from rejection).
I am still not convinced that the contribution is sufficiently solid.
Claims And Evidence: Proofs are provided.
I think that the assumptions are sufficiently restrictive to r... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments, we hope the answers below can address them, and we remain at your disposal for any additional questions.
Regarding examples of problems satisfying the assumptions, note that we provide in Appendix G.2 and G.3 several examples, and also write there why such examples... | Summary: This paper studies a variant iterative hard thresholding (IHT) algorithm for minimizing a smooth objective over support-preserving constraints. The constraint is expressed as the k-l_0 pseudo-ball and a support preserving convex set. In the proposed variant of IHT, the orthogonal projector is approached by the... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments, we hope the answers below can address them, and we remain at your disposal for any additional questions.
Regarding terminology and prior work, by “global convergence” we don’t mean convergence to a global minimizer (which is intractable due to NP-hardness), but rat... | Summary: - Introduce TSP (projected version of IHT) with iteration rule:
$$\omega_{t+1} = \Pi_\Gamma \circ \Pi_{B_0 (k)} (\omega_{t} - \eta \nabla R(\omega_{t}))$$
- Introduce three-point lemma for hard thresholding $(\Pi_{B_0 (k)})$:
If $\bar \omega \in B_0 (\bar k)$ then
$$\|w-\bar w\|^2 \ge \|\Pi_{B_0 (k)} w - w\|... | Rebuttal 1:
Rebuttal: Thanks a lot for your review and appreciation of our work. Regarding practical problems which satisfy the new condition but did not satisfy conditions considered previously, actually our example on portfolio optimization (in appendix G.2) with sector-wise constraints, is such an example: it is not... | Summary: This paper studies with the problem of optimizing a convex function over the intersection of a sparsity ($\ell_0$) constraint and other convex constraints $\Gamma$. The additional constraints are required to be support-preserving, i.e. such that any orthogonal projection onto $\Gamma$ preserves the support. Th... | Rebuttal 1:
Rebuttal: Though we leave a more insightful and in-depth investigation of the tightness of our bound in terms of $\rho$ to future work, the main reason why there is a dependence in $\rho$ is that the original three point lemma is not valid anymore, and an extended version needs to be done, which contains an... | Summary: 1. This paper considers a variant of IHT that addresses sparse optimization problems while incorporating additional convex constraints.
2. The authors propose a two-stage projection gradient method.
3. They evaluate the effectiveness of these approaches under both stochastic and non-stochastic settngs.
Cl... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments, we hope the answers below can address them, and we remain at your disposal for any additional question.
Regarding the comparison with the coordinate-wise optimization method by Beck et al., we cite that paper in our paper but do not compare our convergence rates i... | Summary: This paper considers the problem of minimizing a function subject to a mixed constraints here two. One of the constraint enforces sparsity via the pseudo-norm $\ell_0$ which make this constraints nonconvex and hard to deal with. The try to force the solution to belong into a convex set $\Gamma$. The authors ... | Rebuttal 1:
Rebuttal: Thanks a lot for your comments, we hope the answers below can address them, and we remain at your disposal for any additional questions.
Q1: Assumption 2.3
A1: For Assumption 2.3, we will write it as a definition and state in the theorems that our objects verify such definition.
Q2: The regula... | null | null |
CTBench: A Library and Benchmark for Certified Training | Accept (poster) | Summary: While a number of algorithms for verifying the robustness of neural networks have been developed, it has also been shown that models trained using standard training approaches are often not robust and difficult to certify. Certified Training aims at developing methods which encourage verifiability and robustne... | Rebuttal 1:
Rebuttal: We $\newcommand{\Rj}{\textcolor{purple}{VJvs}}$are happy to hear that Reviewer $\Rj$ finds our work interesting and well motivated, our library and benchmark useful and comprehensive, and our experimental results insightful. Due to the word limit, we address major questions raised by Reviewer $\Rj... | Summary: This paper introduces a novel benchmark for certified training, addressing the inconsistencies in evaluating certifiably robust neural networks. Existing methods suffer from unfair comparisons due to varying training schedules, certification techniques, and under-tuned hyperparameters, leading to misleading cl... | Rebuttal 1:
Rebuttal: We $\newcommand{\Rm}{\textcolor{blue}{mj6P}}$thank Reviewer $\Rm$ for their insightful review. We are happy to hear that Reviewer $\Rm$ finds our work interesting and well motivated, our library and benchmark useful and comprehensive, and our experimental results insightful. In the following, we a... | Summary: The authors proposed to do a new round of meta-research on the topic of certified training (because the previous one [1] became outdated), compared the top algorithms and baselines with a fair training pipeline, and are going to share a library and related benchmark for further usage.
[1] Linyi Li, Tao Xie, a... | Rebuttal 1:
Rebuttal: We $\newcommand{\Rv}{\textcolor{green}{vyCo}}$thank Reviewer $\Rv$ for their insightful and careful review. We are happy to hear that Reviewer $\Rv$ finds our work interesting and well motivated, our experimental results convincing, and our ablation studies insightful. In the following, we address... | null | null | null | null | null | null | null | null |
MA-LoT: Model-Collaboration Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem Proving | Accept (poster) | Summary: This paper proposes the MA-LoT framework, which incorporates a long CoT with an iterative refinement approach to improve formal theorem-proving ability. The model first makes an attempt using NL planning followed by FL proof. If the attempt is incorrect, the initial prompt is combined with the error message to... | Rebuttal 1:
Rebuttal: Dear Reviewer tVG8
Thank you so much for your appreciation of our work. We are sincerely grateful that you consider it suitable for ICML.
With our deepest thanks for your constructive comments, we would like to share the latest results on the MiniF2F-Test dataset using a new base model named **G... | Summary: This paper introduces MA-LoT, a multi-agent framework for theorem proving in Lean 4, integrating natural language (NL) reasoning with formal language (FL) verification via Long Chain-of-Thought (Long CoT). Using a novel LoT-Transfer Learning pipeline, MA-LoT enhances proof coherence and depth, outperforming GP... | Rebuttal 1:
Rebuttal: Dear Reviewer bbL4
Thank you so much for your valuable comments on our paper and your appreciation of our contribution to both dataset construction and training methodology. Your encouragement truly motivates us to continue pursuing research in this field.
Because formal theorem proving is a fas... | Summary: This paper introduces MA-LoT, a multi-agent framework for formal theorem proving in Lean 4 combining natural language reasoning with verifier feedback. MA-LoT employs two "agents" (same LLM prompted in different ways): a prover that generates proofs using "Long" Chain-of-Thought reasoning, and a "corrector" th... | Rebuttal 1:
Rebuttal: Dear Lfxq,
We would like to offer our sincere thanks for your constructive comments and valuable suggestions, which have helped make our paper more coherent.
Because formal theorem proving is a fast-evolving field, we would like to share with you the latest results on the MiniF2F-Test dataset fo... | null | null | null | null | null | null | null | null |
Teaching Physical Awareness to LLMs through Sounds | Accept (poster) | Summary: This paper proposes a method to incorporate physical awareness into LLMs using audio signals, focusing on fundamental acoustic phenomena such as the Doppler effect, multipath reflections, and direction-of-arrival. The authors introduce a specialized simulator that generates large-scale training data for these ... | Rebuttal 1:
Rebuttal: # Response to Reviewer 9yAo
We sincerely thank the reviewer for the thoughtful and constructive feedback. We are encouraged by your recommendation! Our responses are as follows:
---
**Regarding Simulator-to-Reality & Parameter Selection**
**Response:** Our simulation approach follows the princ... | Summary: The paper proposes teaching Large Language Models (LLMs) to understand the physical world through sound. The authors created a physics-based simulator to generate a large audio dataset, AQA-PHY, annotated with physical phenomena like the Doppler effect and spatial relationships. They also developed a novel aud... | Rebuttal 1:
Rebuttal: # Response to Reviewer yVZE
We sincerely thank the reviewer for the thoughtful and constructive feedback. We are encouraged by your recommendation! Our responses are as follows:
---
**Regarding Semantics Understanding of Audio**
**Response:** We agree that semantic understanding is an important... | Summary: The paper introduces an approach to teach physical awareness to large language models (LLMs) through sound, using a physics-based audio simulator to create the AQA-PHY dataset. The dataset consists of 1 million audio-based question-answer pairs capturing phenomena such as Doppler effects, multipath, and spatia... | Rebuttal 1:
Rebuttal: # Response to Reviewer 3nj6
We thank the reviewer for the thoughtful and constructive feedback. We recognize that certain aspects of **our presentation may have led to confusion**, and we appreciate the opportunity to clarify the following key points:
- Our method **does support spatial audio** ... | Summary: The authors train an LLM to have knowledge of the physical world through acoustics. To do this, the authors create a large synthetic dataset of question-answer pairs that include audio from an acoustic simulator. The authors introduce an audio encoder that incorporates phase information, and they show that it ... | Rebuttal 1:
Rebuttal: # Response to Reviewer Gt9i
We sincerely thank the reviewer for the thoughtful and constructive feedback. We are encouraged by your recommendation! Our responses are as follows:
---
**Regarding Physcial Awareness vs. Acoustic Calculator**
**Response:**
Thank you for raising this insightful ques... | Summary: The paper presents a novel method to imbue large language models with physical awareness through sound by using a physics-based channel simulator that synthesizes realistic acoustic data, simulating phenomena such as the Doppler effect, multipath reflections, and LOS conditions. The authors design an audio enc... | Rebuttal 1:
Rebuttal: # Response to Reviewer t8Co
We sincerely thank the reviewer for the thoughtful and constructive feedback. We are encouraged by your recommendation! Our responses are as follows:
---
**Regarding Innovation Compared to BAT**
**Response:** We greatly appreciate BAT's pioneering work in bringing s... | null | null | null | null |
Sortformer: A Novel Approach for Permutation-Resolved Speaker Supervision in Speech-to-Text Systems | Accept (poster) | Summary: This paper proposes a new multi-speaker speech diarization and recognition model, with a loss function bridging timestamps with the tokenzied texts, built on classification loss, permutation invariant loss, and the newly-proposed sort loss. The sort loss looks like an variant of PIL, where the label is placed ... | Rebuttal 1:
Rebuttal: ## Response to Claims and Evidence
1. Sort loss is not a complementary or supportive loss to the system. Only by including Sort Loss, the model learns to arrange speaker predictions in arrival-time order.
a. If we only use PIL, the model does not have arrival-time sorting capability.
b.... | Summary: The authors introduce Sortformer, a model built on a transformer-based encoder and trained using a hybrid loss that combines permutation invariant loss (PIL) and the newly proposed Sort Loss. Sort Loss is formulated as a binary cross-entropy loss, calculated between the sorted speaker presence labels in a sequ... | Rebuttal 1:
Rebuttal: ## Response to Claims And Evidence
### 1. Response to the reviewer's claim that the statement "Sort Loss solves the permutation problem is not fully supported":
a. What we cannot achieve without PIL+ alpha=0.5 is the "maximized diarization performance", not "resolved permutation".
b. Therefore... | Summary: This paper proposes a model called sortformer and a sorting loss to achieve joint speaker diarization and ASR without the need for permutation invariance loss. The proposed model can still be trained with PIL and it can also be combined with the sorting loss. In terms of modeling, the speaker label probabiliti... | Rebuttal 1:
Rebuttal: ## Response to Experimental Designs Or Analyses:
Here is model size information for the models we listed in Table 1. We will add the model size to Table 1.
- **MSDD**: 31.1 M
- **EEND-EDA**: 6.4M
- **WavLM-L + EEND-VC**: 317M+
- **EEND-GLA-Large**: 10.7M
- **AED-EEND**: 11.6M
- **AED-E... | Summary: The paper proposes Sortformer, an encoder-based neural model designed for permutation-resolved speaker diarization integrated into speech-to-text (STT) systems. Its core innovation is the introduction of a Sort Loss that addresses the traditional permutation invariance problem in speaker diarization by sorting... | Rebuttal 1:
Rebuttal: ## 1. Response to Claims and Evidence
### 1-1. Robustness
While the reviewer may find our evaluation insufficient, we believe the robustness of Sort Loss is demonstrated through the datasets we used up to a certain degree:
- **DIHARD3** ("Diarization is Hard") covers 11 challenging domains, inc... | null | null | null | null | null | null |
Distributed Nonparametric Estimation: from Sparse to Dense Samples per Terminal | Accept (poster) | Summary: This paper studies nonparametric function estimation under communication constraints, where each distributed terminal holds multiple i.i.d. samples and can communicate sequentially. The authors establish nearly minimax optimal rates across different regimes and identify phase transitions as the number of sampl... | Rebuttal 1:
Rebuttal: Thanks for your detailed reading and acknowledging the generality of our technique. We will carefully revise our paper according to your suggestions. In the following we provide responses to your questions.
## Responses to the suggestions:
- **Reorganization of the Introduction and Literature ... | Summary: This paper investigates the phase transition in optimal estimation rates as the number of samples per terminal increases from sparse to dense in a distributed setting. The results are purely theoretical, filling gaps in the existing literature and offering a wide range of applications. Overall, the paper is we... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and interesting questions. Here are our detailed responses.
## Responses to the weaknesses:
- **The problem under investigation seems a bit simple. Such one-dimensional problems are way too simple.** $\hat{f}_{Hs}$ **has a straightforward closed-form expression.... | Summary: The paper studies the problem of distributed nonparametric estimation under communication constraints, where each terminal holds multiple i.i.d. samples. The authors characterize the minimax optimal rates across all regimes, covering the transition from sparse to dense samples per terminal. They propose a two-... | Rebuttal 1:
Rebuttal: Thanks for your careful reading and detailed comments. Here are our point-to-point responses to your concerns.
## Responses to the weaknesses:
- **... some assumptions seem slightly more substantial than in prior works (e.g., Zaman & Szabó, 2022). ...**
We want to clarify the rationalities of... | Summary: The authors consider the communication-constrained problem of nonparametric function estimation, in which m distributed terminal observe each n i.i.d. samples drawn from some distribution parameterized by f, and each can send a message of L bits to a central decoder, which is then interested in estimating f un... | Rebuttal 1:
Rebuttal: Thanks for your very positive assessment and the suggestions. We are going to revise our paper according to your suggestions in the following aspects.
1. Adding more details to Sections 3 and 4 on wavelets and upper bounds within the space limitation. The last paragraph of Section 3 that provide... | null | null | null | null | null | null |
Explicit Exploration for High-Welfare Equilibria in Game-Theoretic Multiagent Reinforcement Learning | Accept (poster) | Summary: This paper proposes a strategy exploration method that introduces social welfare maximization into the PSRO framework. By creating an exploration strategy and using it to regularize new strategies, E$\text{x}^2$PSRO tends to discover strategies with higher social welfare when solving the Nash equilibrium. Espe... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments and feedback. As noted, we rely on experiments to validate Ex2PSRO’s faster convergence compared to classical PSRO (see Fig. 6, with more details in App. H). We do not theoretically guarantee Ex2PSRO’s performance. However, note that Ex2PSRO’s response objecti... | Summary: Multi agent RL environments are getting more and more relevant. In many environments in order for solving very different tasks in the real world multiple agents are going to collaborate and solve a task. However, discovering NEs with high social welfare is hard with current RL algorithms. Even more so, discove... | Rebuttal 1:
Rebuttal: Thank you for the insightful comments and feedback. We agree that improvements should be appropriately contextualized to determine improvement magnitude. Please refer to our answer to Q1 of reviewer uMdQ: our bar graph visual range is normalized according to an available distribution of equilibria... | Summary: The authors propose EX2PSRO, which extends PSRO to encourage finding maximum welfare solutions. A regularization target policy is trained by behavior cloning high minimum welfare trajectories collected during best-response training. This policy then acts as a regularizer during SAC best response training, enco... | Rebuttal 1:
Rebuttal: Thank you for the helpful comments and feedback. We agree that any evaluation could benefit from additional benchmarks. However, we believe the four benchmarks (two qualitatively distinct variants on two very different games) cover a very informative range of cases. The artificial MDP example also... | Summary: This paper proposes the Ex2PSRO algorithm, which introduces an explicit exploration mechanism into the PSRO framework to optimize equilibrium social welfare. Specifically, the method constructs exploration policies through behavior cloning on high-welfare trajectories and guides policy optimization via KL-dive... | Rebuttal 1:
Rebuttal: Thank you for the positive comments and feedback. We would like to clarify that Ex2PSRO is not inherently limited to symmetric games—any more than PSRO is. The methods work for non-symmetric games simply by performing strategy generation for each player separately. Assuming symmetry for this paper... | null | null | null | null | null | null |
Steerable Transformers for Volumetric Data | Accept (poster) | Summary: This paper introduces a new equivariant transformer architecture in two variants, with symmetry group either SE(2) or SE(3), utilizing a steerable (Fourier) basis. In numerical experiments, performance gains are shown if some layers in steerable CNNs are replaced by the novel attention layers.
## Update after... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and thoughtful suggestions. We have carefully considered the questions and concerns raised and provide detailed responses below.
**Weaknesses**
We have included comparisons with other methods on both the Rotated MNIST and ModelNet10 datasets in the... | Summary: The authors present an SE(3) steerable attention layer. Specifically, input features are assumed
to be equivariant tensor features in the specific form as is output by SE(3) steerable networks
of (Weiler et al. 2018-2019). The authors key contribution could be understood as a novel form of
positional embedd... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and thoughtful suggestions. We have carefully considered the questions and concerns raised, and provide detailed responses below.
**Patchification**
We would like to clarify the reviewer’s concern regarding "patchification". In ViTs, patchification... | Summary: This paper explores the steerable (SE3 equivariant) vision transformer for volumetric data. The proposed framework was built upon existing steerable convolution and vision transformers. Steerable transformers was evaluated on 2D and 3D classification (rotated MNIST and ModelNet10) and segmentation (skin images... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and thoughtful suggestions. We have carefully considered the questions and concerns raised, and provide detailed responses below.
**Complexity Calculations**
We acknowledge the reviewer’s concern regarding the complexity calculation. It is true tha... | Summary: This work introduces a steerable equivariant transformer operating in the spectral domain. The method calculates attention scores exclusively between Fourier embeddings associated with matching irreducible representations (irreps). These scores are then combined linearly to construct the final attention matrix... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review and thoughtful suggestions. We have carefully considered the questions and concerns raised, and provide detailed responses below.
**Equivariance Error**
We thank the reviewer for the insightful suggestion about equivariance error. In response, we h... | null | null | null | null | null | null |
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning | Accept (poster) | Summary: This paper proposed a diffusion-based maximum entropy RL. The authors first derived the lower bound of entropy by data processing inequality. Then the soft policy iteration is recased to diffusion-based version with new regularization term with the lower bound. The policy optmization is reformulated to the low... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for the many helpful comments and suggestions. We hope the following replies address the questions and concerns raised.
---
> The claim 'significantly outperforming other diffusion-based methods on challenging high-dimensional con... | Summary: This paper introduces DIME, a novel online RL algorithm using diffusion policies. The key innovation lies in proposing a new method for maximizing the entropy of a diffusion policy with more rigorous theoretical justification. The authors establish a lower bound for policy entropy and derive a practical diffus... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for the many helpful comments and suggestions. We hope the following replies address the questions and concerns raised.
---
> The experimental section lacks comparisons with some of the latest diffusion-based online RL methods, su... | Summary: This paper combines maximum entropy reinforcement learning with the diffusion model, leading to a coherent algorithm that addresses several critical problems that exist in previous methods. The main contribution of this paper is that it derives a tractable lower bound of the entropy of the action distribution ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for the many helpful comments and suggestions. We hope the following replies address the questions and concerns raised.
---
> The comparison with baseline methods may not be strictly fair. For non-diffusion methods, hyper-paramete... | Summary: Authors have introduced the idea of using diffusion-based policy in maximum entropy RL. To tackle the issue of intractable entropy calculation of using a diffusion-based policy, the authors derive a computationally tractable lower bound on the maximum entropy objective. Through extensive experiments, the propo... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work and for the many helpful comments and suggestions. We hope the following replies address the questions and concerns raised.
---
> Some ablations is reported on just one environment. It would be better to include all. For example, for ... | null | null | null | null | null | null |
Eliciting Language Model Behaviors with Investigator Agents | Accept (poster) | Summary: The paper presents a method for behavior elicitation in language models. Namely, the authors introduce several methods that given a target generation $y$ or a target rubric (e.g. the generation is harmful), can find inputs $x$ to elicit such outputs. The authors apply these methods to several problems like eli... | Rebuttal 1:
Rebuttal: We thank reviewer VYJr for their positive feedback and have incorporated their suggestions. We address the questions below:
**1. “I expect hallucinations to be a problem when they happen in-distribution (i.e. when users naturally chat with models).”**
We agree that finding natural “in-distributi... | Summary: This paper studies the problem of behavioral elicitation, i.e., searching a prompt that can induce a target language model to output specific target behaviors. The author mainly considers two cases including string elicitation (exact-match) and rubric elicitation (rubric-based verifier). To achieve it, the aut... | Rebuttal 1:
Rebuttal: We thank reviewer BMgr for their time and review. Reviewer BMgr primarily asked for a clarification about the generalization of our investigator model, and for a comparison with gradient-based search baselines. We address these comments below. Please let us know if there are any additional questio... | Summary: The paper “Eliciting Language Model Behaviors with Investigator Agents” presents a method for systematically discovering prompts that elicit specific behaviors—such as hallucinations, harmful responses, or jailbreaks—from large language models (LLMs). The authors introduce investigator models, trained to rever... | Rebuttal 1:
Rebuttal: We thank reviewer jLAY for their time and review. We appreciate the positive review, and will address the typos and table captions you point out in the revision. We address broader comments and suggestions below. Please let us know if there are any additional questions or concerns during the discu... | null | null | null | null | null | null | null | null |
Generalization of noisy SGD in unbounded non-convex settings | Accept (poster) | Summary: This paper provides generalization error and differential privacy guarantees for the stochastic gradient Langevin dynamics algorithm (SGLD). This is done by proving bounds on either a KL divergence (generalization error) or a Renyi divergence differential privacy(). In accordance with several prior works, the ... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your time, your very detailed review and feedback. We will add a discussion of the limitation you mention.
Thank you for spotting the typos, here are a few details to answer some of the remarks:
- Definition A.1: In integral form the expectation is the following:
$... | Summary: The authors introduce novel generalization guarantees for Noisy SGD that extend to non-convex settings, possessing desirable properties such as an $O(1/\sqrt{n})$ dependence on dataset size and an $O(1)$ dependence on the number of iterations. The first major contribution of this paper demonstrates that assumi... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and thorough review of our work. We would like to address some of the points you have raised.
*Sampling directly from the Gibbs distribution*: You are correct in stating that an exact sample from the Gibbs distribution would attain the fast rate. However, ther... | Summary: This paper investigates the stability and generalization properties of noisy stochastic gradient descent (SGD) in unbounded non-convex settings, extending prior analyses that primarily focused on convex or bounded loss functions. The authors establish stability-based generalization bounds that remain non-vacuo... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your thorough review and detailed feedback. We would like to address some of the areas of improvement that have been identified.
**Extension of the bound**:
You correctly observe that some generalization bounds only hold for an evaluation loss (like the 0-1 los... | Summary: This paper studies noisy SGD, or Stochastic Gradient Langevin Dynamics (SGLD), in the setting when they are run for many iterations with non-vanishing step sizes, to understand the generalization bounds. They study this by comparing the weights when run on two different, independently-sampled datasets. They sh... | Rebuttal 1:
Rebuttal: We thank you for your time and review. We would like to address some of your concerns.
We have provided a clear application of our bound in our response to mwzT. Our goal is to have theory catch up to practice where it is already common to train on non-convex losses for several thousand iteration... | null | null | null | null | null | null |
Aligning Protein Conformation Ensemble Generation with Physical Feedback | Accept (poster) | Summary: his paper introduces Energy-based Alignment (EBA), a novel approach that enhances generative models by incorporating feedback from physical models. EBA efficiently calibrates these models to balance conformational states based on energy differences, overcoming intractable optimization issues.
Tested on the MD... | Rebuttal 1:
Rebuttal: We sincerely appreciate the thoughtful feedback and helpful suggestions from the reviewer PSvo. We here address each point raised and clarify aspects of our work where necessary.
1. Additional baselines. We acknowledge the importance of comprehensive baseline comparisons. In our study, we primaril... | Summary: This work presents Energy-based Alignment (EBA), a method to fine-tune a pretrained diffusion model to sample mini-batches of protein structures that match the underlying Boltzmann distribution. This alignment is achieved by minimizing the cross entropy between the ground truth Boltzmann distribution, and the... | Rebuttal 1:
Rebuttal: We kindly appreciate the reviewer yVMH’s supportive and insightful feedback. Below, we address each of the key questions raised and provide clarifications where necessary.
1. Clarifications on theoretical derivation. We acknowledge that the substitution of the denoising loss into the KL divergence... | Summary: This work concerns the problem of improving diffusion models for protein conformation generation using the information from a physical model. It proposes a new fine-tuning loss EBA for diffusion model training, based on the principle of offline RL using energy labels as the a negative reward feedback.
By bal... | Rebuttal 1:
Rebuttal: We sincerely appreciate the detailed and constructive feedback from the reviewer BE4T! We will address each question/concern as follows:
1. Target distribution and bias away. Indeed, given sufficient unbiased MD data, directly maximizing the likelihood (MLE) is ready to converge to the Boltzmann ... | Summary: This paper tackles the task of conformational ensemble generation in protein structure prediction. While folding models like AlphaFold predict individual states given a sequence, the task here is to be able to sample from the entire Boltzmann distribution instead. To this end, the authors propose an alignment ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer Gaxj's detailed feedback and constructive criticism. Below, we address the main concerns raised in the review and clarify points of potential confusions.
1. Clarification of the learning objective. The reviewer raises a point regarding the “approximation of the... | null | null | null | null | null | null |
Optimizing Adaptive Attacks against Watermarks for Language Models | Accept (spotlight poster) | Summary: This work proposes adaptive paraphrasing models as a new attack vector against various LLM watermarks. The adversary primarily targets an adaptive no-box setting, where the watermarking algorithm (but not keys, etc.) is known, but the adversary has no access to the watermarking model itself (e.g., for querying... | Rebuttal 1:
Rebuttal: Thank you for your time, valuable suggestions, and excitement about our paper. Please find our responses below.
> [..] explanations are largely empirical in nature, leaving some uncertainty about where to go from here for future, more robust, watermarks.
Thank you for raising this point, which ... | Summary: The paper proposes to apply preference optimization (DPO) to make open LLM paraphrasers better at removing watermarks from texts. The focus is on the nobox setting (the attacker has no previous access to the LLM API) and both adaptive (scheme is known but not its key) and non-adaptive settings (the exact schem... | Rebuttal 1:
Rebuttal: Thank you for the detailed response and generally positive outlook on our paper. We appreciate your time and effort. Please find our responses below.
> [..] key message of the paper is that training against *any* watermark can improve removal success against the target watermark.
We agree with t... | Summary: The paper investigates the robustness of Large Language Model (LLM) watermarking. While previous research has primarily tested watermarking against non-adaptive attackers (lack knowledge of the watermarking technique), this study introduces an approach by formulating robustness as an objective function and usi... | Rebuttal 1:
Rebuttal: Thank you for your time, consideration, and many valuable suggestions. Please find our responses below.
> Yes, the methods and evaluation are reasonable. However, the main body primarily relies on LLM-Judge for quality evaluation, while the perplexity performance, as shown in Table 3, is not par... | Summary: The paper addresses the vulnerability of Large Language Model (LLM) watermarking methods to adaptive attacks. It argues that existing watermarking robustness tests primarily focus on non-adaptive attackers, which underestimates the risk posed by adversaries with knowledge of the watermarking algorithm. The aut... | Rebuttal 1:
Rebuttal: Thank you for your time and valuable suggestions. Please find our answers below.
> The paper could benefit from a more in-depth discussion of potential defenses against adaptive attacks, such as adversarial training.
We appreciate this suggestion. While we focused on the robustness of current wa... | Summary: The paper tackles the question of robustness of the generated text from LLMs in the offline setting with adaptive attackers. Their approach is evaluated on a wide variety of LLMs and also weaker paraphasers (LLMs) by considering four watermarking techniques recently introduced in the literature.
Overall: The ... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in providing constructive feedback that will improve our paper. Please find our responses below.
> [..] they use standard RL techniques such as DPO to optimize their objective function from a preference dataset.
Yes, we use standard RL techniques for optimizati... | null | null | null | null |
Test-Time Preference Optimization: On-the-Fly Alignment via Iterative Textual Feedback | Accept (poster) | Summary: This paper introduces a method called **Test-Time Preference Optimization (TPO)**, which aims to improve the alignment of large language models (LLMs) with human preferences during inference. TPO builds upon the previous **TextGrad** framework, extending its capabilities to optimize LLM outputs at test time wi... | Rebuttal 1:
Rebuttal: Thank your for your thoughtful and constructive feedback. We are encouraged by the insightful comments and suggestions provided. Regarding your specific concerns:
1. **Simplifying Appendix to Reduce Overlap with the Main Sections**
We will simplify and restructure the appendix, clearly d... | Summary: This paper proposes TPO, an inference-time approach that aligns LLM outputs with human preferences without updating model parameters. TPO improves model outputs by iteratively interacting with reward models, which provide rewards in textual form. The results show that the proposed approach improves LLM perform... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. Below, we address your concerns concisely:
1. **Introducing TextGrad as Preliminaries.**
Thanks for your suggestion. We agree that clearly introducing TextGrad prior to discussing our methodology will enhance readability. We will include a concise... | Summary: The paper introduces TPO, a new alignment strategy as an alternative to RLHF, that only acts at test-time, without modifying the main model's weights. In particular, TPO works by learning a proxy reward model, and iterating between generating N completions from the models, ranking them, and formatting the best... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. Below, we address your concerns concisely:
1. **Revision of Overall Formal Notation**
We acknowledge the confusion caused by the notation in Equation (3) and we will carefully revise these equations. Additionally, we will reduce the use of gradient... | Summary: This paper studies test-time preference optimization by finding responses that maximize the reward model values with verbal reinforcement learning.
Claims And Evidence: Yes, the claims are well-supported.
Methods And Evaluation Criteria: Yes, the instruction-following benchmarks are commonly used in alignmen... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and valuable suggestions. Below, we address your concerns concisely:
1. **Comparison with Test-time Verbal RL**
We appreciate your suggestions providing an opportunity to compare with test-time verbal RL. As recommended, we implemented Reflexion, wh... | null | null | null | null | null | null |
Antidote: Post-fine-tuning Safety Alignment for Large Language Models against Harmful Fine-tuning Attack | Accept (poster) | Summary: This work finds that existing defensive methods against harmful fine-tuning are susceptible to large learning rates and/or training epochs. To mitigate this issue, the authors provide a method that is agnostic to the choice of fine-tuning hyperparameters, Antidote, which is pruning the harmful parameters in th... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback on this paper. Below we address each conern.
> **W1:** Can the authors provide results on some text generation tasks?
Following your valuable suggestion, we consider an additional experiment to show that Antidote does not degrade text generation p... | Summary: The paper focuses on the problem of losing LLM alignment due to harmful data in a dataset used for fine-tuning, especially when large learning rate or high number of training epochs are employed. The paper presents Antidote as a post fine-tuning strategy which removes the harmful parameters via pruning.
Claim... | Rebuttal 1:
Rebuttal: Thanks for your encouraging comments and suggestions. We below first provide the experiment result of the suggested experiments. Later we will use this result to clarify your main concern.
> **W2:** Add a discussion on a potential application workflow where you have users fine-tuning two differe... | Summary: This paper proposes a novel post-fine-tuning approach called "Antidote," designed to defend safety-aligned LLMs against harmful fine-tuning attacks. Antidote identifies and removes harmful parameters by calculating importance Wanda scores on a re-alignment dataset consisting of harmful examples. Extensive empi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review. Below is our response to the concern.
> W1: Antidote does not significantly outperform existing baselines, particularly in finetune accuracy.
It is challenging to **surpass all the baselines** in **both the two metrics** and in **all the attack settings**... | Summary: In this paper, the authors propose a post-fine-tuning defense mechanism to address the issue of harmful fine-tuning in safety-aligned models. The paper first demonstrates that the underlying defense mechanism is highly sensitive to hyper-parameter tuning (e.g., learning rate, training epochs), which is crucial... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback. Each comment is addressed below.
> **Claims + W1+ W2:** Removing parameters leads to a reduction in fine-tune accuracy. This is because you misidentify some benign partners as harmful! How to justify that the identified parameters are not benign?
Our work... | null | null | null | null | null | null |
FEAT-KD: Learning Concise Representations for Single and Multi-Target Regression via TabNet Knowledge Distillation | Accept (poster) | Summary: The paper substitutes the feature search of the SR method FEAT by using the mask of a TabNet to select the important input variable and learn the feature using DistilSR, an exhaustive search method.
Claims And Evidence: The reviewer appreciates that the paper shows the improvement with respect to FEAT (and va... | Rebuttal 1:
Rebuttal: > General
Please see response R1 to reviewer mwAW.
> Claims And Evidence
R19: We thank the reviewer and will add the results for extended datasets in PMLB used in FEAT in Appendix F, “Evaluation on More Datasets”. We pick a representative subset in the main pages because we want to show results ... | Summary: The paper presents FEAT-KD, a method that distills knowledge from a TabNet deep neural network into interpretable symbolic regression models. It replaces traditional genetic programming (GP) used in FEAT with a neural-guided symbolic regression approach, producing concise mathematical expressions. Key contribu... | Rebuttal 1:
Rebuttal: > General
Please see response R1 to reviewer mwAW.
> Experimental Designs Or Analyses
R14: We thank the reviewer for the suggestion, the original FEAT is designed for regression tasks [1] and since a large amount of evaluation was required to verify both single and multi-target regression, we d... | Summary: The FEAT framework typically uses genetic algorithms to derive the concise-feature representations. The linear combination of these concise-representation features is then the predicted output of this model. TabNet is a deep-learning based model for tabular data that can be used for single/multi-target regress... | Rebuttal 1:
Rebuttal: > General
We thank the reviewer for the thorough review and suggestions. We address the comments and implement the suggestions in our individual responses to the reviewers. For convenient referencing to a response, we use the notation R{number} to label a response. We hope that if the responses a... | Summary: This paper introduces FEAT-KD, a method that transfers the strengths of TabNet and FEAT to create concise, interpretable models for both single-target and multi-target regression. The method distills pieces of a trained TabNet into short symbolic expressions using an exhaustive search algorithm (DistilSR). The... | Rebuttal 1:
Rebuttal: > General
R1: We thank the reviewers for the thorough reviews and suggestions. We address the comments and implement the suggestions in our individual responses to the reviewers. For convenient referencing to a response, we use the notation R{number} to label a response (e.g., this response is la... | null | null | null | null | null | null |
Exogenous Isomorphism for Counterfactual Identifiability | Accept (spotlight poster) | Summary: This paper analyses L3 identifiability, showing that full recovery of exogenous variables in SCMs is not required to achieve it. It also unifies the existing theories of bijective SCMs and TM-SCMs, implementing neural TM-SCM for the experimental section, thus showing the practical applicability of the develope... | Rebuttal 1:
Rebuttal: We appreciate the reviewers for raising questions regarding the novelty of this paper. Below are clarifications to address these misunderstandings:
> **Question 1**: Specify the degree of novelty of the paper
The main result of this paper is that if causal mechanisms are aligned via **exogenous ... | Summary: This paper explores ∼L3-identifiability, aiming to ensure that all Structural Causal Models (SCMs) satisfying given assumptions provide consistent answers to causal questions. The authors introduce exogenous isomorphism and propose ∼EI-identifiability, showing that full recovery of exogenous variables is not n... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and for pointing out some weaknesses and suggestions. Below are our responses to these points.
> **Weakness 1**: Limitations of Assumptions
It is acknowledged that the strength of the assumptions is a common drawback in identification tasks; howe... | Summary: The paper introduces the notation of exogenenous isomorphism (EI) between SCMs, a suffient relationship (Theorem 3.2) that ensuring counterfactual equivlance. In section 4 and section 5, the paper focus on two special types of SCMs: Bijective SCMs (BSCMs) and Triangular Monotonic SCMs (TM-SCMs). The paper firs... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing out the typo and have added three references relevant to our work:
- (Brehmer et al., 2022): In this paper, Theorem 1 proves that the latent causal model is identifiable from weak supervision up to graph isomorphisms and elementwise diffeomorphisms, where the la... | null | null | null | null | null | null | null | null |
Learning Progress Driven Multi-Agent Curriculum | Accept (poster) | Summary: The authors apply an automatic curriculum design method, SPRL, to multi-agent reinforcement learning, using the number of agents as a parameter to control task difficulty. They call this method SPRLM.
They further extend SPRLM to maximise temporal difference error, which they call "learning progress", and call... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ffyd
We thank the reviewer for the valuable comments. We note that the main concern comes from the comprehensiveness of the benchmarks and inclarity of the method description. In response, we have conducted additional experiments on new benchmarks, and we hope that the enha... | Summary: The paper presents a curriculum learning method for MBRL, where the task difficulty is controlled by the number of agents, using TD error for learning progress measurement. The method is evaluated on three sparse-reward benchmarks and presents empirical advantages over baselines.
### Update after rebuttals
Th... | Rebuttal 1:
Rebuttal: ## Response to Reviewer p7uW
We thank the reviewer for the appreciation of the novlty of our method. We hope our clarification and new experiments help to address your concerns.
### 1. Clarity on the variance comparison:
> However, the settings of these two figures can be further explained, inclu... | Summary: This paper looks at curriculum learning in multi-agent reinforcement learning (MARL) by using the number of agents as a dynamic context variable. The authors first adapt self-paced reinforcement learning (SPRL) to the multi-agent setting (SPRLM), then propose SPMARL - a more principled variant that measures le... | Rebuttal 1:
Rebuttal: ## Response to Reviewer ZwRM
We thank the reviewer for the appreciation of the simplicity and generality of our method. We hope new experiments and clarifications help to address your concerns.
### 1. Clarity on observability setting:
>If I'm understanding the paper correctly, the setting is full... | null | null | null | null | null | null | null | null |
David and Goliath: Small One-step Model Beats Large Diffusion with Score Post-training | Accept (poster) | Summary: The paper presents a novel online method for alignment fine-tuning of a one-step diffusion-based text-to-image generation model.
Claims And Evidence: * The paper claims to require no data in the abstract. But in reality, it does use a dataset of prompts. I think this is misleading.
* The paper abstract seems ... | Rebuttal 1:
Rebuttal: Dear reviewer, we are delighted that you like the novelty of DIstar for the one-step diffusion model post-training. We appreciate your valuable suggestions. In the following paragraphs, we will address your concerns one by one.
**Q1**. Clarifications on image-data-free property and post-training ... | Summary: This paper proposes Diff-Instruct*, a post-training method to align one-step text-to-image generative models with human preferences. The work is an evolution of “Diff-Instruct” and “Diff-Instruct++,” replacing the KL-based divergence (as in standard PPO) to a score-based divergence for regularization. Empirica... | Rebuttal 1:
Rebuttal: Dear reviewer, we are glad that you like our novelty of regularizing the one-step diffusion model during post-training. In the following paragraphs, we will answer your questions one by one.
**Q1**. Experimental or theoretical evidence that shows mode-collapse issues of one-step diffusion distill... | Summary: The paper proposes Diff-Instruct* (DI*), a new post-training framework for one-step text-to-image generative models, aiming to align their outputs with human preferences without requiring image data. The method leverages score-based reinforcement learning from human feedback (RLHF), optimizing a human reward f... | Rebuttal 1:
Rebuttal: Dear reviewer, we are delighted that you like our score-based reinforcement learning post-training of the one-step diffusion model. We appreciate your valuable suggestions. In the following paragraphs, we will address your concerns one by one.
**Q1**. Clarify the differences between Diff-Instruct... | null | null | null | null | null | null | null | null |
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces | Accept (poster) | Summary: - The paper introduces a framework for multimodal diffusion models on arbitrary state spaces via independent noise schedules for each modality.
- In particular, the paper proposes a theoretically grounded framework for multimodal diffusion of continuous as well as discrete state spaces.
- After training, the d... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable insights.
### Regarding T2I performance
We want to emphasize that our work focuses on training for the next generation of multimodal diffusion models for **multiple tasks** rather than a single one, which in general is much more challenging. Desp... | Summary: This paper proposes a novel diffusion-based framework for both continuous and discrete multimodal data (images and text). Specifically, for continuous image modality, the paper utilizes diffusion process as forward and backward process. For the discrete text modality, the paper uses CTMC to determine the state... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable insights.
### Regarding evaluation benchmarks
We'd like to emphasize that the objective of our work is to introduce a general framework for training multimodal generative models using diffusion, as opposed to being considered a task specific met... | Summary: In this paper, the authors focus on the problem of using diffusion to model multi-modal data domains, especially text and image data. To this end, they propose a novel approach to the noise schedule which is distinct for each modality. They justify their approach theoretically with proofs. They then evaluate t... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable insights.
### Regarding Generality of the set up
Our work built on the general set up of [1] which includes at least Euclidean, discrete, Riemannian and Wright-Fischer diffusions. We improve upon it by leveraging separate noise levels on differe... | null | null | null | null | null | null | null | null |
Robust ML Auditing using Prior Knowledge | Accept (spotlight poster) | Summary: Audits have been historically impactful in AI and they are increasingly becoming a common part of proposals for regulating it. however, it could be possible for developers to game audits so that the model seems to behave much better on the evaluation than real-world cases. This paper discusses this problem, ra... | Rebuttal 1:
Rebuttal: Dear Reviewer Q6QF,
Thank you very much for taking the time to read and review our paper. We are delighted that you found such value in our work and agree that it is a very real problem that developers can predict evaluations.
**How about more general evaluations?**
In this paper, we chose to fo... | Summary: This paper addresses a significant challenge in machine learning fairness auditing: the risk of manipulation (fairwashing) during audits. The authors introduce an approach to make audits more robust by incorporating the auditor's prior knowledge about the task. Through theoretical analysis and experiments, the... | Rebuttal 1:
Rebuttal: Dear Reviewer Ddwq,
Thank you for taking the time to read and review our paper. We are delighted that you found our work to be scientifically sound with a *thorough experimental design* and a *mathematically sound theoretical framework* that also *effectively positions itself within the fairness ... | Summary: The paper studies the problem of robust fairness auditing when the platform can manipulate the model during auditing. To address this problem, they propose to allow the auditor to have access to a set of labeled examples that are close to the prediction of the model before auditing. During the auditing, the au... | Rebuttal 1:
Rebuttal: Dear Reviewer g7Ct,
Thank you very much for taking the time to read and review our paper. We are delighted that you *enjoyed reading the paper* and that, as reviewer Ddwq and Q6QF, you *appreciate the importance of the discussed problem*.
We will answer your points on the security game formulati... | null | null | null | null | null | null | null | null |
Near-Optimal Consistency-Robustness Trade-Offs for Learning-Augmented Online Knapsack Problems | Accept (poster) | Summary: This paper introduces learning-augmented algorithms for the online knapsack problem (OKP) that balance consistency (performance with accurate predictions) and robustness (worst-case guarantees). The authors propose algorithms leveraging succinct predictions (point or interval estimates of the critical value in... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review and your constructive feedback.
**On the small item weight assumption:** We agree that this is a standard and important assumption in the integral setting. Our lower bounds (e.g., Theorem A.1) show that it is necessary for meaningful guarantees in our... | Summary: This paper studies the online (integral/fractional) knapsack problem under the learning-augmented framework. The prediction is either a single value revealing the smallest unit value of items included by the optimal offline solution or an interval containing this value. When the prediction is trusted and items... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive review.
We especially appreciate your recognition of the simplicity and practicality of our prediction models, as well as the value of extending results to the integral setting. We will continue to refine the writing and presentation to further improve cl... | Summary: The paper considers the online knapsack problem with predictions. In this problem, we are given a knapsack and a set of items that arrive sequentially. When each item arrives, its value and weight are revealed, and we must decide immediately and irreversibly whether to place the item in the knapsack. The goal ... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review. You have raised some great points which will help clarify the paper.
**On Theorem 4.2 and randomized algorithms:** Thank you for the careful reading – you are correct that Theorem 4.2 is currently proven only for deterministic algorithms. We will... | Summary: The paper considers the OKP problem based on succinct predictions and design learning-augmented algorithm to achieve a good trade-off between robustness and consistency. The succinct prediction model provides either a single-valued prediction or an interval prediction. The paper first consider trusted predicti... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for highlighting the key question about the robustness of MIX when predictions may be arbitrarily incorrect.
**On the robustness guarantee of MIX despite poor predictions:** The MIX algorithm handles untrusted predictions by combining the decisions of a ro... | null | null | null | null | null | null |
Teaching Transformers Causal Reasoning through Axiomatic Training | Accept (poster) | Summary: This paper proposes axiomatic training, which leverages synthetic data to train small models from scratch. The authors observed that their approach enables models to generalize from small-node to large-node causal structures when evaluated on transitivity axioms and d-separation rules. Moreover, fine-tuning Ll... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the contributions of the work. We answer specific questions below.
>**1. I have some concerns regarding the performance of the small models. Although the authors explored different training strategies and positional embeddings, the results appear to fall sh... | Summary: This paper studies a new method for improving the causal reasoning capabilities of autoregressive transformer text models by training on synthetically generated data containing demonstrations of causal axioms or rules. Specifically, the authors consider the expressions of the form <premise, hypothesis, result>... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the contributions of the work. We answer the specific questions below:
**1. Eval inputs being longer than training text inputs?**
Yes the evaluation set consists of chains longer than the ones in the training setup, and this translates to having longer text... | Summary: This paper propose an approach where the model learns symbolic axioms through demonstrations rather than directly infer causal relationships from data. And then, they investigate whether this approach allows the model to generalize from learning simple causal structures to more complex causal relationships.
C... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating the contributions of our work. We answer specific questions below:
**Response to Weaknesses**
> **1. GPT-4 achieves the best performance, suggesting that causal axioms relation can be learned from unstructured and massive datasets without requiring complex... | null | null | null | null | null | null | null | null |
MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition | Accept (poster) | Summary: The method in the paper attempts to adapt image foundation models for video understanding tasks. The authors introduce MoMa, an efficient adapter framework that integrates Mamba’s selective state space modeling into image foundation models. They propose a novel SeqMod operation designed to inject spatial-tempo... | Rebuttal 1:
Rebuttal: >## 1. Ablation studies focusing on the layer design
Thanks for your advice!
As discussed in Section 3.5, unlike Jamba, which focuses on fine-grained architectural design, our architecture builds upon CLIP and cannot undergo drastic changes. Instead, we focus on how to maximize the advantages of... | Summary: This paper proposed a framework "modulated Mamba" to adapt image foundation models for video understanding tasks by PEFT. There are two stages within the frame work. The first stage is "divide", which runs CLIP feature extraction on the pacthes of each frame. The second stage is "SeqMod" which draws intuiation... | Rebuttal 1:
Rebuttal: >## 1. Whether CLIP is the best model
Note that our MoMa is an adapter method and is thus backbone-agnostic. We choose CLIP as our backbone following previous methods DiST and AIM for fair comparison.
| Method | Accuracy |
| --------- | -------- |
| MoMa-CLIP | 83.7 |
| MoMa-MAE | 81.2 ... | Summary: The paper proposes using Mamba layers as adapters to apply CLIP pre-trained transformer-based image models for video tasks. For each transformer block, the proposed method first divides each frame into multiple windows, applies self-attention within each window, then the tokens of all windows of all frames are... | Rebuttal 1:
Rebuttal: >## 1. Divide part is not justified, using higher resolution inputs.
Thanks for advice! Our original $224 * 224$ comparison aimed to align with other baselines, but we’ve now conducted additional ablation studies at $640*480$ resolution (SD video standard) to address your concern.
**Experiment d... | Summary: This paper presents MoMa, a video foundation model that is built on top of the image foundation model by leveraging Mamba as an efficient adapter. Specifically, Mamba block is used to capture spatial-temporal dynamics without interfering with the pre-trained IFMs. Besides, to avoid excessive computational over... | Rebuttal 1:
Rebuttal: >## 1. Marginal improvement on K400 and SSv2 datasets
We focus on striking a balance between performance and efficiency instead of solely pursuing the highest performance. Besides achieving state-of-the-art performance, we managed to cut nearly 30% calculation FLOPs off with minimum training para... | null | null | null | null | null | null |
An All-Atom Generative Model for Designing Protein Complexes | Accept (poster) | Summary: This paper proposed APM, a protein full-atom sequence and structure co-generation model. The model includes three parts: Seq & BB module, sidechain module and refine module. The learning is based on flow-matching and is achieved in two stages. The paper did a lot of experiments, both on single protein design a... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and insightful comments that have helped us enhance the clarity and quality of our manuscript. We have carefully addressed your concerns below.
**Q1: Claims And Evidence on Complex Design**
A1: Thank you for your questions. We address your concerns as follows:
... | Summary: we introduce APM (All-Atom Protein Generative Model), a model specifically designed for modeling multi-chain proteins. By integrating atom-level information and leveraging data on multi-chain proteins, APM is capable of precisely modeling inter-chain interactions and designing protein complexes with binding ca... | Rebuttal 1:
Rebuttal: We appreciate your helpful feedback. We have responded to your concerns below and look forward to any additional comments.
**Q1: Essential References Not Discussed**
A1: Thank you for highlighting the need to include additional related works. We will enhance our Related Work section by incorpora... | Summary: The paper introduces a new all atom protein backbone generation which is composed of a backbone structure model (that is equivalent to discret flow models from Campbell and al.), a side chain module and a refinement module. They are trained in two stages, first the backbone and side-chain modules separatly and... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We have addressed your concerns below and welcome any further feedback.
**Q1: Relation To Broader Scientific Literature and Essential References Not Discussed.**
A1: We appreciate the reviewer for pointing out these references. In our revision:
1. We will in... | Summary: This paper tackles the problem of designing multi-chain protein complexes at the atomic level. The authors propose APM (All-Atom Protein Generative Model), consisting of three modules:
1. **Seq&BB Module**: A flow-matching based generative model that handles the co-generation of protein sequence and backbone s... | Rebuttal 1:
Rebuttal: We appreciate your thorough review and helpful comments. We have carefully addressed your concerns below and welcome any additional feedback.
**Q1: Claims And Evidence**
A1: Thank you for highlighting the need for statistical validation. We conducted folding with APM and ESM3 using 20 seeds. For... | null | null | null | null | null | null |
Multi-View Graph Clustering via Node-Guided Contrastive Encoding | Accept (poster) | Summary: This paper presented a novel approach to MVGC called Node-Guided Contrastive Encoding. This method addresses the challenges inherent in GNNs for clustering by effectively using homophilic and heterophilic information within graph data. The proposed framework uses node features to guide the embedding process, t... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive critiques on our manuscript. Below, we provide a point-by-point response to your comments.
**Q1: Time Complexity Concerns**
**A1:** We appreciate the reviewer’s attention to computational efficiency. While generating *V* views iteratively ... | Summary: This paper introduces Node-Guided Contrastive Encoding (NGCE), a novel framework for multi-view graph clustering that integrates homophilic and heterophilic information through node-guided contrastive learning. NGCE aims to outperform existing methods by emphasizing node feature-based information and avoiding ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive critiques on our manuscript. Below, we provide a point-by-point response to your comments.
**Q1: Delayed Definition of Mathematical Symbols**
**A1:** We sincerely apologize for the oversight in deferring symbol definitions to the appendix.... | Summary: This work primarily focuses on integrating homogeneous and heterogeneous information in graph data into a unified framework. Its core modules are an edge and node embedding similarity matrix sensitive to graph homophily, a contrastive learning-guided graph encoding mechanism driven by the recovery of noise-enh... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback and constructive critiques on our manuscript. Below, we provide a point-by-point response to your comments.
**Q1: Theoretical Validation or Detailed Experimental Evidence of Section 3.1.2**
**A1:** Thank you for your valuable feedback. In the current versio... | null | null | null | null | null | null | null | null |
3D-LMVIC: Learning-based Multi-View Image Compression with 3D Gaussian Geometric Priors | Accept (poster) | Summary: The paper presents 3D-LMVIC, a learning-based multi-view image compression framework that leverages 3D Gaussian Splatting (3D-GS) as a geometric prior for accurate disparity estimation. Unlike traditional methods that rely on 2D projection-based similarities, this approach improves disparity estimation in wide... | Rebuttal 1:
Rebuttal: Thanks to you for the valuable comments. We are grateful for your positive feedback, especially regarding the model performance. We address your remaining concerns as follows:
### R1[The training time of the 3D Gaussian]
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For the *Train* scene of the Tanks&Temples dataset, which contains 301 i... | Summary: In this paper, 3D-LMVIC is proposed as a novel learning-based multi-view image compression framework, which relies on 3D Gaussian
Splatting to derive geometric priors for accurate disparity estimation. In details, for each image, a depth map is derived from a trained 3D Gaussian. Then the disparity between vie... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We are grateful for your positive feedback, especially regarding the proposed methods and the ablation experiments. We address your remaining concerns as follows:
### R1[Clarify alignment verification and target view selection]
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Given a reference view and ... | Summary: The paper proposes a learning-based multi-view image compression framework, 3D-LMVIC, which utilizes the 3D Gaussian geometric prior for disparity estimation. Through experiments, its advantages in compression efficiency and disparity estimation accuracy have been verified.
Claims And Evidence: Please see Oth... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We are grateful for your positive feedback, especially regarding the algorithm's running speed and compression performance. We address your remaining concerns as follows:
### R1[Test on Cityspace, KITTI, and Instereo2K]
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**1. Limitations of Stereo Images f... | Summary: This paper targets on the multi-view image compression task. The main contribution includes a Gaussian Splatting-based disparity estimator for wide-baseline images, a depth map compression model to minimize geometric redundancy, and a multi-view sequence ordering strategy to enhance correlations between adjace... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments! We are grateful for your positive feedback, especially regarding the quality of writing, theoretical justifications, and experimental results. We address your remaining concerns as follows:
### R1[Use of median over weighted average for depth estimation]
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... | null | null | null | null | null | null |
Learning Imbalanced Data with Beneficial Label Noise | Accept (poster) | Summary: This paper proposes a new data-level approach called Label-Noise-based Re-balancing (LNR) to solve the imbalanced learning issue. LNR utilizes the introduction of asymmetric label noise to adjust decision boundaries and improve classifier performance, particularly for minority classes. Unlike existing approach... | Rebuttal 1:
Rebuttal: **Reply to Information Loss caused by label flipping in Claims And Evidence and Weakness 2**
- We sincerely appreciate your insight on potential information loss due to label flipping. In LNR, the majority-class samples selected for flipping are primarily outliers that have deeply encroached into ... | Summary: In this paper, the authors study the problem of class imbalance. To be specific, they propose using asymmetric label noise in favor of the minority classes to mitigate the bias on the decision boundary between majority and minority classes. To this end, the authors formulate Bayesian optimal decision boundarie... | Rebuttal 1:
Rebuttal: **Reply to Relation To Broader Scientific Literature & Essential References Not Discussed & Weakness 1 and 2**
- We sincerely appreciate you providing these books and the two recent articles—they are very insightful. We recognize that some of these prior studies share similar conclusions with the... | Summary: The paper introduces a novel method called Label-Noise-based Re-balancing (LNR) to address imbalanced classification problems by incorporating beneficial label noise. This approach involves flipping labels of majority class samples to minority classes to adjust decision boundaries and enhance classifier perfor... | Rebuttal 1:
Rebuttal: **Reply to Claims And Evidence and Methods And Evaluation Criteria**
Thank you for your thoughtful comments. We appreciate your feedback and would like to clarify that our evidence sufficiently supports our claims.
- Our theoretical analysis focuses on the deviation between the optimal F1 decis... | Summary: This paper introduces a novel Label-Noise-Re-balancing (LNR) approach to mitigate the decision boundary bias caused by data imbalance.The numerical experiments in both binary and multi-calss imbalance demonstrated the effeciency of their approach.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes... | Rebuttal 1:
Rebuttal: **Response to ‘Essential References Not Discussed’， Weakness 1 and Question 1**
We sincerely thank the reviewer for highlighting the need to include recent methodological advancements in our literature review. As suggested, we will carefully revise the 'Related Work' section in our manuscript:
... | null | null | null | null | null | null |
GenZSL: Generative Zero-Shot Learning Via Inductive Variational Autoencoder | Accept (poster) | Summary: This paper proposes a novel generative paradigm for zero-shot learning (GenZSL), which is based on the idea of induction rather than imagination. To ensure the generation of informative samples for training an effective ZSL classifier, GenZSL incorporates two key strategies, e.g., class diversity promotion and... | Rebuttal 1:
Rebuttal: **Response:**
Thank you for the comprehensive reviews and detailed comments! We are very happy to help to address your concerns!
**Q1:** If there are not similar classes for the unseen classses in the seen class set, the method may out of work?
**A1:** Thank you for this constructive comment. I... | Summary: This paper introduces GenZSL, a novel inductive framework for generative zero-shot learning (ZSL) that addresses limitations in existing generative ZSL methods. Traditional approaches generate visual features "from scratch" using expert-annotated class semantic vectors, leading to suboptimal performance and p... | Rebuttal 1:
Rebuttal: **Response:**
Thank you for the comprehensive reviews and detailed comments! We are very happy to help to address your concerns!
**Q1:** The claim for CDP lacks quantitative validation (e.g., semantic similarity metrics pre/post-CDP)
**A1:** The quantitative validation for CDP is presented belo... | Summary: This paper introduces GenZSL for generative zero-shot learning. It first employs a class diversity promotion module to reduce redundant information in class semantic vectors. Additionally, a semantically similar sample selection module is used to select referent class samples. Experiments conducted on three po... | Rebuttal 1:
Rebuttal: **Response:**
Thank you for reviewing our submission and the comments! Here are our responses to your concerns:
**Q1:** Can the authors provide evidence that these vectors are also more diverse?
**A1:** In Fig. 3 and Fig. 8, we present the class semantic vectors’ similarity heatmaps that are ext... | null | null | null | null | null | null | null | null |
Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs | Accept (poster) | Summary: The paper proposes a proxy measure of multiplicity defined as the difference in average prediction of the model on a hypersphere of radius $\sigma$ around the point $x$ and the mean absolute difference between predictions on the hypersphere and the point $x$. The paper shows that this measure strongly correlat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review!
Link to **PDF** with new Figures and Tables: https://drive.google.com/file/d/1zMdT0zdMrIPO9eUCHGYZ-WUlYsgbyDYu/view
---
**Additional Re-trainings:** We have included an additional experiment with 100 retrainings (see Table 11 in PDF) and still see a high ... | Summary: This paper addresses the challenge of fine-tuning multiplicity in tabular LLMs, where minor variations in training (e.g., seeds, hyperparameters) lead to conflicting predictions across equally performant models. The authors propose a stability measure, which quantifies prediction robustness by analyzing the ... | Rebuttal 1:
Rebuttal: Thank you for your positive review!
Link to **PDF** with new figures and tables: https://drive.google.com/file/d/1zMdT0zdMrIPO9eUCHGYZ-WUlYsgbyDYu/view
---
**Regarding strong assumptions:** Our theoretical analysis draws inspiration from standard assumptions in optimization and statistical lear... | Summary: This paper studies the problem of fine-tuning multiplicity in tabular LLMs, where models trained from the same pre-trained checkpoint under different conditions (e.g. random seeds) make inconsistent predictions on the same inputs. The authors propose a new measure called consistency to estimate the robustness ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review!
Link to PDF with new figures and tables: https://drive.google.com/file/d/1zMdT0zdMrIPO9eUCHGYZ-WUlYsgbyDYu/view
---
**Regarding downstream use**: While our work focuses on quantifying the stability of predictions—to our knowledge, the first to do ... | Summary: The work studies the problem of model multiplicity in LLM classification for tabular data. Model multiplicity refers to the phenomenon that multiple models of similar accuracy assign confliciting predictions to individual instances. The authors propose a measure of model multiplicity (that does not require ret... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review!
Link to **PDF** with new figures and tables: https://drive.google.com/file/d/1zMdT0zdMrIPO9eUCHGYZ-WUlYsgbyDYu/view
---
**Regarding Mean and Variability Term:** Our stability measure relies upon both local variability and mean confidence because they captu... | null | null | null | null | null | null |
Reward-Guided Speculative Decoding for Efficient LLM Reasoning | Accept (poster) | Summary: The paper introduces Reward-Guided Speculative Decoding (RSD), an improved version of SD which replies on a process reward model to determine the quality of a step instead of exact match. RSD combines a lightweight draft model with a more capable target model, integrating a reward-based mechanism to optimize c... | Rebuttal 1:
Rebuttal: $~$
**Supplementary Material:** https://anonymous.4open.science/r/Rebuttal-supp-6595-C4A2/appendix_rebuttal_ICML.pdf
$~$
---
**Q1.** Why RSD outperforms the large model?
> Please refer to our rebuttal to **Reviewer p3vx Q5**.
$~$
**Q2.** Alternative reward models or potential biases
> As w... | Summary: This paper introduces Reward-Guided Speculative Decoding (RSD), a novel framework designed to improve the efficiency of inference in large language models (LLMs) by combining a lightweight draft model with a more powerful target model. Extensive evaluations on challenging reasoning benchmarks demonstrate that ... | Rebuttal 1:
Rebuttal: **Q1.** The reliance on a process reward model, which may introduce additional overhead.
> This is indeed a very practical concern, since RSD utilizes one more model, i.e. the process reward model (PRM), than speculative decoding (SD). However, accoding to our experiments, the overhead is minor f... | Summary: This paper introduces a method to guide speculative decoding using a reward model. Unlike standard speculative decoding, where a larger model verifies the outputs of a smaller model, the proposed approach determines acceptance or rejection based on reward signals. Specifically, the authors design a series of w... | Rebuttal 1:
Rebuttal: **Q1.** PRM vs large models’ likelihood
> The core issue is the misalignment between the large model (LM) and PRM, leading SD to reject high-quality tokens due to low LM likelihood. While the reviewer suggests this implies high FP rates from LM, these are often style-/format-related, not correctn... | null | null | null | null | null | null | null | null |
How Transformers Learn Structured Data: Insights From Hierarchical Filtering | Accept (poster) | Summary: The authors propose a synthetic way to evaluate how Transformers learn interactions on trees that are generated with different positional correlations. They show that the Transformer learns to approximate the algorithm used to generate the synthetic data. They show that the Transformer learns "longer range" in... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and address their comments and questions.
On the referee’s concerns towards the ‘surprise’ of our results: Let us clarify why we believe that our findings are not trivial. While one of the paper’s conclusions is indeed that, in the end, the tran... | Summary: Transformer architectures have become highly successful in deep learning, achieving state-of-the-art performance in various NLP and computer vision tasks. However, it is still not fully understood how transformers learn from different types of data. This paper takes a step toward a better understanding of tran... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback and address here the weaknesses and questions they have raised.
- On the weakness about the lack of theoretical analysis: We are indeed unable to derive precise analytical results in our paper (as the complexity of our data models implies that we do not even ... | Summary: This paper investigates how a vanilla transformer encoder learns to infer latent hierarchical structure from data. The authors introduce a *synthetic* hierarchical tree-structured data model with a tunable filtering parameter $k$ that controls the depth of correlations in the sequence. Using this controlled se... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback, and answer the points that they have raised.
- On the weakness point about the data being far from real-world one: We do agree that the data we used is far from, say, natural language. However, the complexity of real data strongly limits one’s understandi... | Summary: The paper studies how simple transformer models learn to perform root and leaf inference (corresponding to classification and masked-language modeling tasks) on a synthetic generative hierarchical model of data on a regular tree of depth $\ell$, belonging to the class of context-free grammars. For such a model... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading our work and providing valuable feedback.
On the efficiency of the BP implementation within the transformer and the MLP role: We had omitted to include an additional point in the Appendix, which is a precise proposition for performing the update of Eq. ... | null | null | null | null | null | null |
DexScale: Automating Data Scaling for Sim2Real Generalizable Robot Control | Accept (poster) | Summary: The paper introduces a data engine that automatically simulates and scales skills for learning robot manipulation policies. In particular, DexSim presents a comprehensive pipeline for Sim2Real data scaling by automating domain randomization and adaptation processes. The authors claim that this approach not onl... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely appreciate your constructive feedback. We hope that the following response can address your concerns:
> 1. "*...substantial manual adjustments are required to align the hand and object models within the simulation before retargeting them to the end-effector pose. ... F... | Summary: The paper proposes a new data generation pipeline, that takes human video demo as input and generate retargeted data for robot manipulation. The pipeline involves different stages: scene projection, action-trajectory projection, scene simulation, action-trajectory simulation and various techniques to bridge th... | Rebuttal 1:
Rebuttal: Dear Reviewer, We sincerely appreciate your constructive feedback and thank you for recognizing the significance of our work. We have carefully considered your suggestions, and we hope that the following response can address your concerns:
> 1. *"...I only see simple tasks like grasping and open ... | Summary: This paper proposes DexSim, a pipeline for automating the learning of manipulation skills from human videos. Given an egocentric video, it first extracts human hand, wrist, and object pose trajectories. It then retargets the hand trajectory to a robot gripper and finds an object mesh closest to the object in t... | Rebuttal 1:
Rebuttal: Dear Reviewer, we sincerely appreciate your constructive feedback. We hope that the following response can address your concerns:
> 1 "*Only a few supplementary videos show different robots, there is no quantitative comparison in the main text or supplementary material.*"
**Response.**
Table 4 ... | null | null | null | null | null | null | null | null |
Provable Length Generalization in Sequence Prediction via Spectral Filtering | Accept (poster) | Summary: - This paper considers the problem of length generalization for sequence prediction in linear dynamic systems.
- They define a new notion of regret, called Asymmetric Regret, which measures the difference in cumulative loss between an algorithm that is only allowed to use information in the past $L$ time poin... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and detailed review!
Connection to LLMs Response
You are correct - we have zero theory for LLMs, and unfortunately this is not uncommon, our theoretical understanding of LLM is very limited. We see this as a start of a theory for length generalization for *any* sequ... | Summary: The paper introduces a novel theoretical framework addressing length generalization in sequence prediction tasks using spectral filtering methods. It defines a new metric, Asymmetric-Regret, that quantifies the regret of predictors trained with shorter contexts against those trained with longer ones. The autho... | Rebuttal 1:
Rebuttal: Thank you for a thoughtful and detailed review!
Q1 (Realistic Systems)
This is a good question. On the face of it - linear dynamical systems are a toy mathematical model, and it is unclear if real dynamics are linear or symmetric. However: 1) our bounds do not depend on the hidden dimension, so... | Summary: The authors consider the problem of length generalization in sequence prediction, i.e., whether online time series prediction algorithms can learn long-range dependencies using only a short context window during training. Despite its importance in areas like LLMs, the current literature offers few theoretical ... | Rebuttal 1:
Rebuttal: Thank you so much for your thoughtful and detailed review!
Q1 (noisy setting)
Our theorem extends to the case of stochastic noise with bounded variance. To extend to the case of adversarial noise, the norm of the allowed noise must be bounded with inverse to $T$. We present our results in the n... | Summary: The authors study an online sequence prediction problem where the sequence is generated by a time-invariant linear dynamical system within a class of spectral filtering predictors. They introduce a notion of regret between spectral filters that use context length $L$ and the full context, i.e., length $T$. Two... | Rebuttal 1:
Rebuttal: Thank you so much for your thoughtful and detailed review!
Q1 (L vs T)
In the paper we distinguish between L and T, where L is the context length and T is the overall sequence length. It is possible to take L=T. You are right that $\phi_i$ has size $T$ but we only allow ourselves to look at $L$... | null | null | null | null | null | null |
Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence | Accept (poster) | Summary: This paper introduces MODEL SWARMS, a collaborative search algorithm for adapting large language models (LLMs) through principles of swarm intelligence, leveraging collective behaviors to guide individual systems. Inspired by Particle Swarm Optimization (PSO), MODEL SWARMS optimizes collaboration among diverse... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments and feedback.
> It would be beneficial to include a convergence analysis of the proposed update mechanism to better understand the theoretical properties and stability of the optimization process.
We investigate convergence and st... | Summary: This paper proposes a Particle Swarm Optimization based Large Language Model collaborative search algorithm, where LLM weights are considered as particles and PSO is applied to search for a best performing LLM on a target task. Experimental results show that the searched LLMs outperform the initial LLMs and ot... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments and feedback.
> Besides, the proposed method does not provide the alignment method for LLMs with different sizes, which may limit the further application of Model Swarms.
> Usually the initial expert LLMs for different fields have... | Summary: - This paper introduces Model Swarms, a search algorithm to adapt LLM capabilities
- The model is based on particle swarm optimization (PSO)
- Model Swarms (MS) = multiple LLM experts collaboratively search for new "adapted models"
- The purpose of these newly adapted models is to search for capabilities beyo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments and feedback.
> Will the code be released publicly upon publication?
Yes, along with the best-found model checkpoints for all tasks. | Summary: The authors propose utilizing a population of base language models (with the same architecture but different weight initialization) and then study how to finetune them for downstream utilization. Their method consists of treating each model as a particle in the weight space, assigning random "exploration" velo... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their thoughtful comments and feedback.
> How does the computational cost of ModelSwarms compare with baselines?
The main computational cost comes from model inference and evaluating the LM checkpoints on the utility function, most simply performance on a ... | null | null | null | null | null | null |
Residual TPP: A Unified Lightweight Approach for Event Stream Data Analysis | Accept (poster) | Summary: Residual Temporal Point Process (TPP) is introduced as a novel method for event stream data analysis, unifying statistical and neural TPP approaches through Residual Events Decomposition (RED). RED uses a weight function to quantify how well the intensity function captures event characteristics and identify re... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful feedback!
>Q1：Is the threshold $w$ arbitrarily chosen? If not how do you choose a good value?
Please refer to our response to Reviewer **H11P**'s Q2.
>Q2：Why the proposed stepwise approach is lightweight?
Residual TPP follows a 3-step procedure：(1) fi... | Summary: The paper proposes decomposing a TPP into two models - one is a traditional model like Hawkes and the other is a neural model. First, Hawkes model is fit to the sequence. Then the residual events are found using an influence function. Neural model is fit to the residual points and the overall model is the sum ... | Rebuttal 1:
Rebuttal: Thank you for your insightful and positive feedback!
>Q1：Is there any alternative to using an influence function?
Please refer to our response to Reviewer **H11P**'s Q1 and Table1 in https://anonymous.4open.science/r/ResidualTPP-3695/Re_H11P.pdf.
>Q2：This also seems like a novel way of finding... | Summary: The paper proposes Residual TPP, a hybrid framework combining classical statistical TPPs (e.g., Hawkes processes) and neural TPPs through Residual Events Decomposition (RED). This computationally efficient approach leverages Hawkes processes for self-excitation/periodicity and neural TPPs for residuals, reduci... | Rebuttal 1:
Rebuttal: Thank you for the detailed suggstions.
>Q1：Why is $\phi'(x)$ defined with piecewise quadratic decay instead of smoother alternatives?
We acknowledge this concern and have incorporated new experiments using function $\phi'(x)=\frac{(1+\alpha)(x+1)}{(x+1)+\alpha\exp(x)}$，which is smooth across its... | null | null | null | null | null | null | null | null |
Memory Layers at Scale | Accept (poster) | Summary: This paper proposes an improved Memory layer design, which adds extra parameters to the model without increasing FLOPs. Following Memory layer design of previous works, this paper optimizes embedding bag kernels and improves architectures by gating networks for performance and QKNorm for stability.
In the ex... | Rebuttal 1:
Rebuttal: Thank you for the kind review! We will try to address the comments in the order they were raised.
## MoE Results
While we cannot fully explain the poor performance of MOE models (especially for the 1.3b setting), we can provide the following additional information:
- Our MOE implementation reuses... | Summary: The paper proposes a trainable key-value look-up to embed within the transformer architecture as an inductive bias for memory. To make this computationally efficient, they propose ways to parallelize the search across GPUs and show that this architectural change helps factuality.
### Update after rebuttal
Whi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the supportive feedback! For benchmarks beyond factuality, we tried to include a variety of standard benchmarks on table 2 for the 8B models. In addition to this, here are results for the GSM8K math benchmark that we recently ran (8B, 1 trillion tokens):
| GSM8K exact m... | Summary: This paper describes a scaling analysis of memory layers and a comparison with alternative sparsely activated layers like MoEs and PEER. The main claims that the paper makes are:
1. Performance improves by increasing the size of the memory layers.
2. Memory layers significantly outperform dense layers.
3. Memo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review. Please find our answers and clarifications below:
## Evidence for claims
> it would be helpful to include a similar plot for non-factual tasks (e.g. HumanEval or MMLU)
We opted to not provide numbers for these benchmarks for the small model siz... | Summary: This paper proposes to replace the feed-forward layer in LLM with a memory layer. A memory layer consists of a key and a value matrix. Similar to the attention mechanism, each token representation will attend to the top-k selected values. Since it's sparsely activated, the computation cost will be much lower t... | Rebuttal 1:
Rebuttal: Thank you so much for the positive feedback! We will do our best to improve based on the reviews. Here is how we plan on addressing the comments:
## Experimental details
We will add an appendix with the details about the experimental setup including model dimensions and training hyper-parameters.... | Summary: The authors conduct LLM scaling experiments in which the dense FFNs in a transformer are replaced with "Memory Layers". A Memory Layer uses the attention operation to attend over a block of trainable parameters. The advantage of attention over a traditional MLP, is that it is possible to implement sparse var... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful comments! We will try to address the feedback in order of priority, starting with the discussion of performance as this was deemed to be the “biggest weakness” of the paper.
## Discussion of performance
> The biggest weakness of this paper is tha... | null | null | null | null |
RLTHF: Targeted Human Feedback for LLM Alignment | Accept (poster) | Summary: This paper presents Sargy, a hybrid framework designed to align LLMs with human preferences by integrating LLM-generated annotations and selective human feedback. The framework operates iteratively, identifying erroneous samples through reward model distributions, prioritizing difficult cases for human annotat... | Rebuttal 1:
Rebuttal: Thank you for your recognition and constructive review of our work!
**Q1:** The identification of "elbow" and "knee" points in the curve is heuristic and may not always correspond to clear boundaries between correctly and incorrectly labeled samples.
**Re:** We acknowledge the practical concern ... | Summary: This paper proposes Sargy, a human-AI hybrid framework that combines LLM-based initial alignment with selective human annotations to achieve near-human annotation quality with minimal effort. The reward model's distribution is used to identify hard-to-annotate samples mislabeled. Then it iteratively enhances d... | Rebuttal 1:
Rebuttal: Thank you for your recognition and constructive review of our work!
**Q1:** Other variants that are e.g., Greedy where the upper-left or bottom-right samples of the reward distribution are being annotated
**Re:** This will indeed be an interesting factor to compare quantitatively in the final ve... | Summary: The paper introduces Sargy, an iterative human-AI hybrid framework for aligning large language models (LLMs). The core idea is to leverage a reward model to identify data points that are difficult for an AI to label consistently with human preferences and then to selectively solicit human feedback on these cha... | Rebuttal 1:
Rebuttal: Thank you for your recognition and constructive review of our work!
**Q1:** Comparison with a simple filtering strategy
**Re:** Thanks for the relevant reference! The RIP paper was first published right around the submission deadline (1/30), and we will cite it in our final version. After carefu... | Summary: This paper introduces Sargy, a human-AI hybrid framework designed to improve LLM alignment with user preferences while minimizing human annotation costs. Sargy strategically combines LLM-generated labels with selective human corrections, identifying and refining mislabeled samples using a reward model’s distri... | Rebuttal 1:
Rebuttal: Thank you for your recognition and constructive review of our work!
**Q1 - Data complexity:** The paper claims that using 15–20% of the data achieves performance comparable to using the full dataset. How does this generalize to more complex datasets?
**Re:** The generalizability of Sargy across ... | null | null | null | null | null | null |
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback | Accept (poster) | Summary: This paper addresses the problem of adversarial attack in contextual dueling bandits. The authors propose a new algorithm (RCDB) that integrates uncertainty-weighted maximum likelihood estimation to mitigate the impact of adversarial feedback. They obtain a near-optimal regret bound that is robust to adversari... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We will address your concerns.
**Q1**: Empirical results for RCDB-S
**A1**: We will add the empirical results for RCDB-S in our revision. As an example, the performance under the greedy attack setting (as described in Section E.1) is summarized in the table ... | Summary: The paper considers the problem of average regret minimization in stochastic contextual dueling bandits under the linear transitivity model and *strong* corruption. The authors first propose RCDB, a robustified version of MaxInP (Saha, 2021) that computes a weighted MLE, and proves its regret bound of $\wideti... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We will address your questions one by one.
**Q1**: Typos and suggestions
**A1**: Thank you for pointing these out. We will address them in our next revision.
---
**Q2**: The writing of lower bound
**A2**: There seems to be a misunderstanding regarding ou... | Summary: The paper considers the adversarial corruption setup in Dueling bandits and proposes an algorithm using the uncertainty-weighted maximum likelihood estimation and provides regret bounds and empirical evaluations.
Claims And Evidence: Yes all theoretical claims have proofs and experimental results are provided... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback! We will address your concerns.
**Q1**: Direct extension of He et al. 2022 using standard analysis techniques from the Duelling Bandits
**A1**: We believe that the reviewer overlooks several significant contributions in our work. First, we carefully analyze t... | Summary: The paper studies the contextual dueling bandits with adversarial feedback, where a strong adversary may manipulate the preference label to mislead the agent, and the number of adversarial feedback is bound by $C$.
The authors propose an algorithm named RCDB to solve the problem. RCDB utilizes uncertainty-weig... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We will address your concerns one by one.
**Q1**: mismatch between motivation and model: the reward function is assumed to be linear, which oversimplifies the complex, high-dimensional reward structures typically used in LLM training
**A1**:
We believe that our moti... | null | null | null | null | null | null |
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck | Accept (poster) | Summary: This paper proposes a method, IBCircuit, for discovering circuits based in the information bottleneck method. Specifically, by interpolating between the original activation and the mean batch activation of a component or edge, and learning these coefficients for all nodes or edges simultaneously, one can disco... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our paper and providing constructive feedback. We would like to address your questions and concerns below.
>**Claims and Evidence**: **Q1**: Not sure what claims of avoiding manually constructed counterfactual activations mean. **Q2**: In t... | Summary: This paper proposes a method to identify circuits -- interpretable subgraph of a computation graph of a neural net -- via the information bottleneck principle. The key idea of the paper's' technique is estimating the IBCircuit objective via noise injection. Empirical results on several well-known tasks, such a... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our paper and providing constructive feedback. We would like to address your questions and concerns below.
>**Claims and Evidence**: **Q1**. Discuss the causality of circuit identification. **Q2**. Sampling methods in previous circuit studi... | Summary: This paper addresses the recent surge of interest in discovering circuits inside language models that are sufficient for faithfully explaining the behavior on specific tasks. The key contribution is to provide a method grounded in the Information Bottleneck (IB) method, which is optimized directly (avoiding ex... | Rebuttal 1:
Rebuttal: We sincerely appreciate your effort in reviewing our paper and providing constructive feedback. We would like to address your concerns below and revise the corresponding parts in the revised version.
**Theoretical Claims:** We would like to further summarize the IBCircuit. IBCircuit can be regard... | Summary: This paper explores circuit discovery in pretrained language models. The proposed method, i.e., IBCircuit, leverages the information bottleneck principle to holistically identify and optimize circuits without needing specific corrupted activations for different tasks. It is demonstrated that IBCircuit can iden... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our paper. We appreciate your time and effort in reviewing our work. We would like to address your questions and concerns below.
>**Weakness 1**: Unclear explanation of "distorted information flow" in Eq.1: Why use input corruption instead of direct KL min... | null | null | null | null | null | null |
Safety Certificate against Latent Variables with Partially Unidentifiable Dynamics | Accept (poster) | Summary: This paper proposes a safety certificate against latent/unobservable latent variables that cause a distribution shift between offline learning data and online execution. The paper aims to efficiently ensure long-term safety for stochastic systems. The safety certificate in probabilistic space can construct a f... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts and valuable feedback. Please find below our response to your comments.
References Not Discussed:
Wang et al. 2023
We will cite this paper. This paper studies the problem of training a safe policy, to be continuously used, when the data with complete state... | Summary: The paper addresses the challenge of ensuring long-term safety in control systems that have latent (unobserved) variables causing partially unidentifiable dynamics and distribution shifts between offline (training) and online (deployment) data. Traditional safety assurance methods typically assume full knowle... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts and valuable feedback. Please find below our answers to your questions.
Q1, Q2.
The required offline dataset and the estimation procedures for $Q^{\pi}$ depend on the specific choice of causal RL techniques. For example, the Q-estimator in Shi et al. 2024 is p... | Summary: This paper proposes a probabilistic safety certificate design methodology for systems with latent variables, aiming to address the safety verification challenges faced by traditional control methods in scenarios involving partial observability and distribution shifts. The core framework involves:
1. **Specia... | Rebuttal 1:
Rebuttal: We sincerely appreciate your efforts and valuable feedback. Please find below our answers to your questions.
Question regarding discrete action:
Equation (42) and its neighbors explain the technique of Shi et al. 2024. N can be used to estimate $Q^\pi$ in our problem. While this specific method ... | null | null | null | null | null | null | null | null |
Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries | Accept (poster) | Summary: This paper presents two extensions to the Transolver architecture, for learning PDEs in systems with high resolution data. First, it introduces changes in how the weights for the "physical states" are computed, to achieve more peaky distributions. And second, it presents a Multi-GPU implementation of Transolve... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer oAjG for your instructive reviews.
> **Q1:** It's not clear what the goal exactly is. The problem might be something else entirely: the distribution over output physical states.
**(1) Our goal is to local-adaptively control each point’s state distribution.**
As stated in... | Summary: This paper introduces Transolver++, an extension of Transolver, designed to handle million-scale geometries in PDE solution operator learning. Building on the physics attention mechanism proposed in Transolver, which learns underlying physical states, this work presents two key advancements-a local adaptive me... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer VjMs for your detailed and instructive review.
> **Q1:** How generalizable is the proposed method across different geometries, particularly in experiments involving car designs from the DrivAerNet++ dataset? Could you provide more details on the statistical distribution of... | Summary: Authors improve the scaling characteristics of a previously proposed model - Transolver - by analyzing computational and performative bottlenecks of the original model:
- homogeneous latent tokens (physical states) => inability to capture mesh details;
- memory bottleneck caused by processing large-scale mes... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer Ma1w for your invaluable suggestions.
>**Q1:** Lack of baseline range on large geometries benchmarks, such as UPT, GraphViT, and PointTransformer. Also, compare with imperfect baseline traditional solvers.
**(1) New baselines.**
Thank you for your constructive and helpfu... | Summary: This paper extends Transolver, which is a efficient transformer that predict the PDE solution of the input discretized geometry and physical quantities. The original Transolver weighted average the intermediate features at each grid node into several physical states tokens for efficient self-attention (physics... | Rebuttal 1:
Rebuttal: Many thanks to Reviewer dnan for providing a detailed and in-depth review.
>**Q1:** Reclarification of the concept of "Physical States," particularly in early layers.
Thank you for your rigorous review.
**(1) "Physical states" is from Transolver (Wu et al., 2024)**
In Transolver, "physical sta... | null | null | null | null | null | null |
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems | Accept (poster) | Summary: This paper introduces Knowledge-Aware Bayesian Bandits (KABB) as a novel framework for improving dynamic expert coordination in multi-agent systems, which defines a five-dimensions of information of knowledge distance function, and leverages a dynamic Bayesian MAB algorithm together to select expert subset for... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful suggestions to improve the quality of our paper.
**Q1**: Supplementary Material
**A1**: The supplementary material is included at the end of the paper. Please let us know if there are any issues accessing it.
---
**Q2**: The importance of each model component
**A... | Summary: This paper introduces KABB, a framework for multi-agent system coordination with knowledge graphs. It addresses issues in large language models and multi-agent systems with a knowledge distance model and dynamic Bayesian MAB framework. Experiments on multiple benchmarks show its high performance and cost-effec... | Rebuttal 1:
Rebuttal: Thank you for recognizing the value of our contributions.
**Q1**: The motivation for formulas.
**A1**: We appreciate the opportunity to clarify the motivation, necessity, and uniqueness of our definitions.
- **Knowledge Distance (Eq. 4)**: Our formulation integrates semantic, structural, and hi... | Summary: This paper proposes a graph-guided router based on the knowledge-aware Thompson sampling strategy for the mixture of agents. The methods and experiments have their merits but still lack some key comparisons and discussions.
Claims And Evidence: There is sufficient evidence for the claims.
Methods And Evaluat... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and insightful comments.
**Q1**: Trying LLM-based representations in ablation study.
**A1**: To further justify the superiority of the Knowledge-Aware module, we replace it with the recently open-sourced SOTA method, EmbedLLM (ICLR 2025) [r1], and denote it as Em... | Summary: In this paper, authors propose Knowledge-Aware Bayesian Bandits (KABB), a model that improves multi-agent system coordination through semantic understanding and dynamic adaptation. There are three key contributions in the work: a three-dimensional knowledge distance model for deep semantic understanding, a dua... | Rebuttal 1:
Rebuttal: Thank you for recognizing our contributions and novelty.
**Q1**: More detailed architecture of KABB in Figure 2
**A1**: Sorry for the confusion. We have refined Figure 2 in the revised paper. Please see our [repository](https://github.com/KABBAnonymous/KABB_icml774_SupplementaryMaterials?tab=rea... | null | null | null | null | null | null |
Score-based Pullback Riemannian Geometry: Extracting the Data Manifold Geometry using Anisotropic Flows | Accept (poster) | Summary: The manifold assumption states that data often reside in low-dimensional submanifolds of the original ambient space. The primary goal of this work is to learn the structure of this low-dimensional manifold and identify the intrinsic dimensionality. This is solved by considering unimodal densities obtained by d... | Rebuttal 1:
Rebuttal: ### **Weaknesses**
**1.)** Refer to our response to Q1.
**2.)** While our parametric assumption indeed focuses on unimodal distributions of the form $p(\mathbf{x}) \propto e^{-\psi(\varphi(x))}$, we observed encouraging robustness in multimodal settings, as demonstrated by the MNIST dataset. Alt... | Summary: This paper introduces a novel score-based pullback Riemannian geometry framework to extract the intrinsic geometry of data manifolds using anisotropic flows. The key contributions include:
1. Score-Based Riemannian Metric: Defines a data-driven Riemannian structure where geodesics pass through high-density re... | Rebuttal 1:
Rebuttal: 1.) By “respecting” the data distribution, we mean that the geodesics induced by the metric traverse regions of high data density. For a rigorous mathematical formulation of this concept please refer to our answer to your second question. This property naturally extends to all considered manifold ... | Summary: The paper proposes a novel framework for learning the intrinsic geometry of data manifolds using pullback Riemannian metrics induced by an anisotropic normalizing flow. The key idea is to model the data manifold with a Riemannian autoencoder (RAE), where the encoder function provides a pullback metric through ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their engagement. Below we address major concerns while clarifying our core contribution: A novel Riemannian geometry framework with closed-form geodesics that provably traverse high-density regions, overcoming limitations of prior data-driven approaches.
- **Clarificat... | Summary: This paper proposes to construct a Riemannian structure from unimodal probability densities. Under a specific condition, the constructed pullback Riemannian structure turns out to be related to that obtained from the score function (i.e., the gradient of the log probability density with respect to data). The p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive constructive feedback! See below for a discussion on the weaknesses:
- **“The paper only considers a simple setting based on unimodal probability densities. This would limit the capability of the proposed method to construct complex geometries.”**
- Thi... | null | null | null | null | null | null |
Closed-form Solutions: A New Perspective on Solving Differential Equations | Accept (poster) | Summary: This work presents an approach to discover analytical solutions of PDEs using reinforcement learning. The authors combine several concepts in this work, including an optimization method constrained by IC/BCs, an iterative construction approach which generates solution skeletons and subsequently refines paramet... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions, we will add clarification where needed and include suggestions as space permits. Extra results: [here](https://anonymous.4open.science/r/SSDE-A47B/SSDE_icml2025_rebuttal.pdf). All figures/tables are from this link.
**Q1: Add the experiments to support ... | Summary: The paper proposes a deep learning approach to obtain closed-form solutions for PDEs. The authors exploit this task through a Markov decision process and introduce an RL-based methodology. They also address acceleration and multi-dimensional problems, presenting an ablation study to support the proposed method... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions, we will add clarification where needed and include suggestions as space permits. Extra results: [here](https://anonymous.4open.science/r/SSDE-A47B/SSDE_icml2025_rebuttal.pdf). All figures/tables are from this link.
**Q1: Advantages of Formulating PDE S... | Summary: This paper proposes SSDE, a reinforcement learning-based framework for deriving closed-form symbolic solutions to differential equations. The authors introduce a risk-seeking constant optimization technique and recursive exploration strategy to enhance the method's efficiency. Experiments are conducted on vari... | Rebuttal 1:
Rebuttal: Thank you for the feedback and suggestions, we will add clarification where needed and include suggestions as space permits. Extra results: [here](https://anonymous.4open.science/r/SSDE-A47B/SSDE_icml2025_rebuttal.pdf). All figures/tables are from this link.
**Q1: How does SSDE compare to other... | null | null | null | null | null | null | null | null |
Towards characterizing the value of edge embeddings in Graph Neural Networks | Accept (poster) | Summary: *Updates after rebuttal: I have increased my score since my concern was addressed by the authors.*
———
This paper studies the benefits of using edge embeddings in graph neural network (GNN) as opposed to node embeddings. The authors theoretically show that under memory constraints on the embeddings, an edge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and we appreciate the positive comments! Below, we address the reviewer’s concerns and questions:
**Efficient approaches that close the separation between edge-based and node-based?** Thanks for pointing us to the reference by Topping et al. on gr... | Summary: The paper focuses on message-passing that also consider edge embeddings. The authors show theoretically that edge embeddings can have substantial benefits in terms of how deep a model needs to be and run some experiments to verify this claim.
Claims And Evidence: While the contributions of the work are mainly... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and we appreciate the positive comments! Below, we address the reviewer’s concerns and questions:
**Interpretation of our main conclusions:** The reviewer is correct that it’s unsurprising that adding edge embeddings may provide additional (repres... | Summary: This paper studies how edge-based embeddings, rather than the more conventional node-based embeddings, can influence the representational power and performance of graph neural networks (GNNs). The authors formalize two message-passing models (one that maintains node embeddings, and another that maintains edge ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and we appreciate the positive comments!
Regarding the *computational challenge posed by dense graphs*: we agree that mitigating this challenge while maintaining the representational power of edge-based architectures is an interesting direction f... | Summary: The authors explore when edge embeddings are more effective than the traditional node embeddings approaches in graph processing. Their theoretical findings suggest that node-based message passing struggles with certain tasks, especially under tight memory constraints, whereas edge processing offers a more effi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and we appreciate the positive comments, particularly that the reviewer finds our work to be “of significant interest to the graph machine learning community”!
Regarding the *size of the performance gain for edge-based GNNs on the real-world exper... | null | null | null | null | null | null |
Near-Optimal Decision Trees in a SPLIT Second | Accept (oral) | Summary: The paper proposes three algorithms, SPLIT, LicketySPLIT, and RESPLIT, building from a common underlying technique to train near-optimal decision trees efficiently.
On one end of the decision tree training spectrum stands greedy algorithms, which are extremely fast but might create sub-optimal decision trees.... | Rebuttal 1:
Rebuttal: Thank you for your review! We really appreciate your feedback on the organization - since ICML allows an additional page for accepted submissions, we’d be happy to move some of the intuition for greedy splits near the leaves, as well as a discussion of RESPLIT and the Rashomon Set, from the append... | Summary: The paper introduces a family of algorithms called SPLIT (SParse Lookahead for Interpretable Trees) for decision tree optimization. These algorithms aim to bridge the gap between the scalability of greedy methods and the accuracy of optimal decision tree methods. The key idea is to use dynamic programming with... | Rebuttal 1:
Rebuttal: > The "Blossom: An Anytime Algorithm for Computing Optimal Decision Trees" paper by Demirović et al. (2023) is a relevant work that is not cited or discussed in the submission. Both papers address the challenge of finding optimal decision trees, with a focus on improving scalability and finite-tim... | Summary: This paper proposes a decision-tree search method for producing near-optimal decision trees in an efficient way. The authors use a look-ahead mechanism to quickly evaluate tree candidates. The authors demonstrate their method in terms of loss, runtime, and Rashomon set search accuracy.
Claims And Evidence: Ov... | Rebuttal 1:
Rebuttal: **Question 1 (How does the distribution of data affect the runtime of the method)**
We’ve given standard worst-case runtime analysis in our theory section, which gives the worst case runtime even for adversarial data distributions. Even for an adversarial dataset that requires high tree complexit... | Summary: Authors propose a decision tree learning method fast like greedy trees and precise like optimal ones. For that they call an optimal solvers only for some subtrees during construction of the overall tree. The paper is well-explained and well-written. Topic is important. The claims are not supported properly by ... | Rebuttal 1:
Rebuttal: Your core concerns seem to focus on handling continuous features, number of repetitions, and showing results with training objective. We address those points in detail below. In brief, our method is fully compatible with continuous features, our results are robust to (even more) multiple repetitio... | null | null | null | null | null | null |
A Unified Approach to Routing and Cascading for LLMs | Accept (poster) | Summary: The paper proposes a unified framework termed "cascade routing" that integrates routing and cascading strategies to optimize the selection of large language models (LLMs) based on a cost-performance tradeoff. It gives a theoretically grounded method using linear optimization to derive optimal routing and casca... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We are happy to hear that they found our claims well supported, our theoretical reformulation fresh and neat, and our experiments convincing. Below, we address their questions.
**Can you clarify how $\gamma$ is determined and whether step-specific $\gamma_j... | Summary: This paper studies how to use multiple LLMs to improve overall performance under budget constraints. The key idea is to combine two popular approaches, model routing and model cascade. The authors start with analyzing model routing, and then generalizes this analysis to multiple rounds of model routing, which ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed review. Below, we address their remaining questions.
**Why is the expectation of the maximum but not just $q_i$?**
There are two primary reasons for using the expectation of the maximum. First, while the model $m_i$ is often stronger than the model $m_{i... | Summary: Existing routing and cascading serve as two distinct strategies for LLMs. This work provides a theoretical analysis of the optimality of existing routing strategies and further proposes cascade routing that integrates both routing and cascading as a theoretically optimal strategy. Cascade routing frames the pr... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We are pleased that they appreciated our optimality analysis of the strategies, our comprehensive discussion of quality estimation, and the demonstrated improvement of our cascade algorithms over baselines. Below, we address their remaining questions.
**Cou... | Summary: The paper proposes to combine cascading and routing, two common approaches for inference with multiple LLMs. The authors formulate each as an optimization problem and then solve it to derive optimal routing and cascading approaches. Finally, they propose "cascade routing" which is a generalized optimization fo... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review. We are happy to hear that they found our experiments valid, our approach novel, and that we provide a better understanding of cascading. Below, we address their remaining questions.
**What is the role of the superscript (j) in Theorem 2?**
The superscript... | null | null | null | null | null | null |
Diffusion Adversarial Post-Training for One-Step Video Generation | Accept (poster) | Summary: This paper proposes an adversarial finetuning framework for one-step T2I and T2V generation for the flow denosing model, which introduces the insights of designing the discriminator and the model and how to stabilize the model training. And it achieves an exciting performance compared with other few-step model... | Rebuttal 1:
Rebuttal: **Video Comparisons**
We would like to clarify that existing research on diffusion acceleration has primarily focused on the image domain. To the best of our knowledge, no prior studies have proposed high-resolution, one-step video generation methods, and consequently, no suitable baselines exist... | Summary: The paper introduces Adversarial Post-Training (APT), a method that accelerates diffusion-based video generation from multiple inference steps to a single step while preserving high-quality visual output. The approach builds on a pre-trained diffusion model and uses direct adversarial training with real data.
... | Rebuttal 1:
Rebuttal: **Related Works**
We appreciate the reviewer pointing out the related works. We will add all of them in the revised paper.
* SF-V and OSV are 1-step image-to-video generation. We initially did not include them because our work focuses on text-to-video generation. Both of these works are based on... | Summary: The paper presents a post-training approach to transform a pretrained video diffusion model (based on DiT architecture) into a one-step generation model, unlike traditional diffusion models requiring multiple (or at least a few) steps. Unlike many existing distillation methods that train a separate student mod... | Rebuttal 1:
Rebuttal: **Comparison with Consistency Baseline**
We respectfully point out that consistency distillation (CD, including methods such as LCM) has been extensively studied in prior works (e.g., DMD, DMD2, Lightning, Hyper-SD, LADD), which consistently show that CD struggles to produce sharp results in a si... | null | null | null | null | null | null | null | null |
Can We Predict Performance of Large Models across Vision-Language Tasks? | Accept (poster) | Summary: The paper discusses a very interesting problem, i.e., predicting the performance of MLLMs. This task is very practical because evaluating LLMs is expensive. Specifically, the authors focuses on the problem that, given a performance matrix with missing value, whether we can fill these missing ones.
## Update a... | Rebuttal 1:
Rebuttal: > **Novelty of the paper**
Thank you for your question! We would like to highlight three main contributions that reflect the novelty of our work.
First, we propose and formulate the problem of LVLM performance prediction based on known performance scores. Previous works on efficient evaluation n... | Summary: This paper introduces a novel framework to predict unknown vlm benchmark scores based on partial observation, from other LVLMs or tasks.
The problem is formulated as a matrix completion task, and the author proposes to apply probabilistic matrix factorization (PMF) with MCMC for this.
The key challenge of t... | Rebuttal 1:
Rebuttal: > **The computational cost**
*Q: Evaluation cost with the newest LMMs-Eval implementation.*
Thank you for your suggestion! We would like to clarify vllm was integrated into LMMs-Eval after our submission. We did not intend to exaggerate anything.
In the rebuttal, we do not have enough time to r... | Summary: This paper formulates the problem of predicting Large Vision-Language Model (LVLM) performance on unseen benchmarks as a sparse matrix completion task. The authors propose using Probabilistic Matrix Factorization (PMF) to predict model performance across datasets that haven't been evaluated yet. The paper intr... | Rebuttal 1:
Rebuttal: > **Metric Validity**
Thank you for your suggestion! We include the following ranking-based metrics.
**Spearman’s rank correlation.**
**Kendall rank correlation.**
**Precision@K.** The proportion of the predicted top 1 model that fall within the top K positions of the ground-truth ranking. Fo... | Summary: The paper proposes a new framework for predicting the performance of large vision-language models across various tasks using probabilistic matrix factorization (PMF) with Markov chain Monte Carlo (MCMC). The framework formulates performance prediction as a matrix completion task, constructs a sparse performanc... | Rebuttal 1:
Rebuttal: > **Explanation to our method**
*Q: Why to formulate it as a matrix prediction problem? What is the underlying principle or intuition?*
We are inspired by recommender systems. Imagine we are recommending movies to users: there are many users and many movies, but each user only rates a few movies... | null | null | null | null | null | null |
Harmonizing Geometry and Uncertainty: Diffusion with Hyperspheres | Accept (poster) | Summary: Standard diffusion models have relied heavily on the simple isotropic Gaussian noise in the forward process to effectively transform an unknown complex data distribution to this simple Gaussian distribution and has proven to be effective for a large variety of tasks. However, despite this effectiveness, many r... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our visual inspections, especially Figure (6) of the submitted paper. Further, we thank the reviewer for the insightful questions and feedback. We have addressed all the questions asked by the reviewer; kindly follow this link: https://tinyurl.com/44sftcu8 t... | Summary: This paper explores the distributional assumptions made by denoising diffusion models and proposes exchanging the traditional Gaussian noise for a von Mises-Fisher distribution on a d-1-dimensional hypersphere. This choice somewhat improves the performance of the diffusion model in generative tasks, especially... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's recognition of our well-reasoned and justified claims, as well as our intuition on magnitude and direction in hyperspherical space. We have added responses to the reviewer’s comments, and for respective Tables and Figures, kindly follow the link: https://tinyurl.com/44... | Summary: The paper introduced an idea to generate data defined on hyperspheres. When data is decomposed into magnitude and direction components, the generation results can be improved.
Claims And Evidence: In Sec 3, the authors mentioned facial datasets several times. However, the method seems to be working with gener... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the thorough evaluation and the relevance of our proposed metrics for hyperspherical geometry. We also appreciate the recognition of our diverse dataset selection, which demonstrates the robustness of our approach. Please refer to the corresponding Tables an... | Summary: This paper introduces a diffusion model on hyperspherical space with hypersperical data and hypersperical noise distribution (von Mises-Fisher, vMF distribution). The forward process with vMF noises keeps the latent samples on the hypersphere. The reverse process is designed accordingly.
Claims And Evidence: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for constructive feedback and for recognizing the motivation of rethinking the noise distribution. Please find our detailed responses below. All Tables and Figures are available at: https://tinyurl.com/44sftcu8.
**Advantage of Matching Noise and Data Manifold:** Unlike tradi... | null | null | null | null | null | null |
DPCore: Dynamic Prompt Coreset for Continual Test-Time Adaptation | Accept (poster) | Summary: This paper proposes DPCore for Continual Test-Time Adaptation. It integrates a Visual Prompt Adaptation for efficient domain alignment, a Prompt Coreset for knowledge preservation, and a Dynamic Update mechanism. Extensive experiments on four benchmarks demonstrate that DPCore outperforms existing CTTA methods... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback and address the specific concerns raised:
## Q1. Hyperparameter Analysis
We determine hyperparameters using four disjoint validation corruptions from ImageNet-C and CIFAR10-C. The same hyperparameters (detailed in Sec 4.1, Appendix C.3) are used across all exp... | Summary: This paper introduces DPCore, a novel approach to Continual Test-Time Adaptation (CTTA) that addresses challenges in dynamically changing environments where domains recur with varying frequencies and durations. DPCore employs Visual Prompt Adaptation for efficient domain alignment, a Prompt Coreset for knowled... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their positive feedback and insightful questions. We appreciate the thorough review and the recommendation to accept our paper. Below, we address the specific questions and concerns raised:
## Q1. Memory Efficiency Analysis
In Lines 216–219, we compare with [1] ... | Summary: This paper utilizes a dynamic prompt coreset (DPCore) for continual test-time adaptation (CTTA). DPCore involves three components: visual prompt adaptation, prompt coreset, and a dynamic update mechanism for either updating the existing or, creating new prompts based on how similar the prompt is to the ones in... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their careful reading of our paper and constructive feedback. Below, we address the specific concerns and questions raised:
## Q1. Source Data Requirement
We appreciate this concern and would like to clarify:
1. DPCore requires source data only before adaptatio... | Summary: The paper focuses on continual test-time adaptation. Under a more complex setting where domains recur with varying frequencies and durations, the paper proposes DPCore with a dynamically updated prompt coreset for the adaptation on different distributions. The experiments on both common continual test-time ada... | Rebuttal 1:
Rebuttal: We thank the reviewer and address each point:
## Q1. Value of Proposed CDC Setting
Our CDC setting models real-world scenarios with irregular distribution shifts where domains recur unpredictably (Fig.1), unlike existing CTTA approaches that assume uniform changes. For example, an autonomous vehic... | null | null | null | null | null | null |
TMetaNet: Topological Meta-Learning Framework for Dynamic Link Prediction | Accept (poster) | Summary: The authors propose a meta-learning framework that leverages the topological information to guide parameter updates of GNN for dynamic graphs. Specifically, it uses the epsilon-net algorithm to select a set of landmark nodes from the complete graph and construct the Dowker Complex. Then it can use the DZP to c... | Rebuttal 1:
Rebuttal: **Q1**: A possible weakness is some of the baselines are not very new. Ablation, sensitivity, and robustness studies under the ROLAND setting ?
**A1**:We have added DeGNN [1] (ICLR23') as a baseline, with experimental results shown below. ∗ indicates statistical significance (p-value < 0.05).
*... | Summary: The paper proposes TMetaNet, a topological meta-learning framework for dynamic link prediction. Key contributions include:
(1) Dowker Zigzag Persistence (DZP): A method combining Dowker complexes and zigzag persistence to efficiently capture high-order topological features in dynamic graphs.
(2) TMetaNet Archi... | Rebuttal 1:
Rebuttal: **Q1**: High-order graph information.
**A**: For higher-order information, we mean various types of graph (sub)structures formed by interactions of multiple nodes simultaneously. Why is this information important and when? Suppose, we design a certain fraudulent scheme for money laundering. To co... | Summary: The paper introduces TMetaNet, a meta-learning framework leveraging topological information for dynamic link prediction. The authors integrate Dowker Zigzag Persistence with graph neural networks to capture evolving topological structures. The work demonstrates competitive performance across six datasets compa... | Rebuttal 1:
Rebuttal: **Q1**: This approach, while innovative, may not fully address the complexity of evolving graph structures.
**A1**: We agree that our approach may not fully address the full complexity of evolving graph structures. However, we argue that given the currently existing methods, we achieve almost as ... | Summary: This paper proposes TMetaNet, a topological meta-learning framework for dynamic link prediction that integrates DZP to capture high-order topological features in dynamic graphs. The authors claim that DZP provides a computationally efficient and stable representation of dynamic graph evolution, which is then u... | Rebuttal 1:
Rebuttal: **Q1**: Lemma B.3.
**A**: To clarify briefly: given two tripods
$R_1:\mathcal{G}^X \leftarrow W_1 \rightarrow \mathcal{G}^Y$ and $R_2:\mathcal{G}^Y \leftarrow W_2 \rightarrow \mathcal{G}^Z$ , each satisfying temporal consistency, their composite tripod is defined via fiber product: $W=\{(w_1,w_2... | null | null | null | null | null | null |
IMPACT: Iterative Mask-based Parallel Decoding for Text-to-Audio Generation with Diffusion Modeling | Accept (poster) | Summary: This paper proposes a framework to achieve high-quality and high-fidelity audio synthesis in text-to-audio generation tasks, by combining iterative mask parallel decoding with continuous latent diffusion model while maintaining efficient inference speed.
Specially, it applies iterative mask parallel decoding... | Rebuttal 1:
Rebuttal: ## Response to reviewer UxfA
- **Missing references**: Thank you for bringing this to our attention. We will include additional references to relevant TTS literature in the final version, placing our work more firmly in the broader context of speech and audio generation.
- **Methodological contri... | Summary: This paper proposes IMPACT, a text-to-audio generation model that balances quality and speed via a hybrid mask-based decoding diffusion architecture. During inference, IMPACT utilizes a masking scheduler to iteratively generate latent embeddings, where each embedding is generated via diffusion modeling.
Claim... | Rebuttal 1:
Rebuttal: - **Human evaluation**
- **Are samples are cherry-picked?**: No. In the original subjective evaluation presented in the paper, we randomly selected some text descriptions from the testing split. To maintain diversity and avoid redundancy, we ensured that the evaluation set excluded text prompt... | Summary: The authors adapt the recently proposed Masked Autoregressive Models (MARs) from [Li et al. 2024] to text-to-audio generation. This architecture is essentially a MaskGIT model with a lightweight diffusion head to enable generating continuous data from an audio autoencoder instead of discrete tokens. The author... | Rebuttal 1:
Rebuttal: ## Response to reviewer RpS3
Thank you for the suggestions. Here are our responses.
1. **Standard error for subjective evaluation**: Following the guideline of rebuttal, we merged the concerns of missing standard error values and confidence intervals in subjective evaluation in our response to Re... | Summary: The paper introduces IMPACT, a text-to-audio model combining masked generative modeling with diffusion models. The main result is the computational efficiency of the proposed method. IMPACT has a significantly lower latency compared to prior work, while being on-par in terms of objective quality and better in ... | Rebuttal 1:
Rebuttal: ## Response to reviewer JkUy
Thank you for your insightful comments and suggestions. Our responses to specific concerns are detailed below:
1. **Confidence intervals in subjective evaluation results**: The 95% confidence intervals (CI) for the subjective evaluation results are as follows (values ... | null | null | null | null | null | null |
Test-Time Adaptation with Binary Feedback | Accept (poster) | Summary: The paper introduces binary feedback-guided test-time adaptation (BiTTA), a novel TTA framewrok designed to adapt deep learning models to domain shifts at test time using binary feedback from human annotators. The authors address limitations in prior active TTA methods, which suffer from high annotation costs,... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer R67W for the comprehensive review and for highlighting both the strengths and potential areas of improvement in our paper. We appreciate your recognition of our reduced labeling costs and dual-path optimization framework.
**Open-sourcing.** We truly agree with your sug... | Summary: The common test-time adaptation methods focus on sample selection through softmax probabilities and further minimize the uncertainty-based loss on the target data. Different from this, the paper proposes to use binary feedback for test-time adaptation to determine adaptation. In contrast to the existing overal... | Rebuttal 1:
Rebuttal: We thank reviewer SHhn for the detailed review and thoughtful questions that helped us improve our work. We appreciate your recognition of our novel problem setup and methodology.
**Clarification regarding Fig. 1 caption.** Traditional TTA methods indeed struggle with severe distribution shifts,... | Summary: The paper introduces BiTTA, a novel test-time adaptation (TTA) framework that leverages binary feedback (correct/incorrect) from annotators to address domain shifts. The key contribution is a dual-path optimization strategy combining reinforcement learning (RL)-guided adaptation on uncertain samples (BFA) and ... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer QyDK for the positive feedback on our work and for recognizing our paper's dual-path optimization strategy and its contributions.
**Delays or inability to obtain timely feedback.** During the rebuttal, we conducted an additional experiment where active adaptation algor... | Summary: This paper explores a new setting of test-time adaptation, in which the authors introduce binary human feedback for test-time learning. The authors introduce MC-dropout for samples’ confidence estimation and then devise a unified test-time RL learning framework to exploiting both Human Feedback Rewards (for un... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer wnVe for the thoughtful evaluation of our work and recognition of our novel problem setting and technically sound framework.
**Clarification on Implementation Details.** While we provided the source code and additional implementation details in $\text{\color{blue}Appe... | null | null | null | null | null | null |
One Example Shown, Many Concepts Known! Counterexample-Driven Conceptual Reasoning in Mathematical LLMs | Accept (poster) | Summary: The paper presents CounterMath, a novel benchmark to assess and enhance LLM's ability to reason through counterexamples in mathematical proofs. Inspired by the pedagogical method of learning by counterexamples, the work introduces a dataset of university-level mathematical statements requiring counterexample-d... | Rebuttal 1:
Rebuttal: ## Response to Reviewer tdTp
Thank you for your efforts in the review, and hope our response will address your concerns!
> Supported by out-of-distribution (OOD) evaluations, but additional benchmarks from formal theorem proving could further validate this claim.
**To further validate our appro... | Summary: The paper targets solving mathematical problems with LLMs, and focuses specifically on proofs by counterexample. The authors introduce a novel benchmark: CounterMATH. It contains 1216 examples of mathematical statements that are (dis)proved via showing a counterexample. The pipeline of creating the dataset is ... | Rebuttal 1:
Rebuttal: ## Response to Reviewer vkWZ
> **Claim (2) has weak experimental evidence: only one model fine-tuned, evaluated on two math benchmarks.**
Our primary goal is to introduce a benchmark and **explore LLMs’ math capability in providing counterexamples.** We observed that even state-of-the-art LLMs s... | Summary: The paper presents new mathematical benchmark which tests the counterexample-based proof generation ability of LLMs across several sub-areas of math. The overall experimental results show that today's LLMs generally have low scores when trying to solve these problems with counter-example based reasoning. Furth... | Rebuttal 1:
Rebuttal: ## Response to Reviewer bVLs
We sincerely appreciate your efforts in the review and hope our response will address your concerns.
> Did the authors look at EN-based textbooks for such proofs?
Yes. While our annotators are native Chinese speakers and thus did not annotate directly from English t... | Summary: The paper introduces a benchmark called COUNTERMATH. It is designed to assess the ability of LLMs to reason about mathematical statements and justify them using counterexamples. The dataset comprises statement-rationale pairs, sourced from math textbooks, and undergoes manual filtering to ensure quality. The e... | Rebuttal 1:
Rebuttal: ## Response to Reviewer KrNF
> No comparison with model fine-tuned on dataset of non-counterexample-based math proofs.
We agree that such a comparison would further validate our approach. Since our primary motivation was to explore how LLMs use counterexamples in proofs, we initially evaluated o... | Summary: The paper studies the capability of LLMs in providing counterexamples for mathematical proofs. Specifically, the paper proposes a benchmark of university level natural language theorem statements along with their rationale and correctness. The authors evaluate a wide range of LLMs on the F1 score and the count... | Rebuttal 1:
Rebuttal: ## Response to Reviewer H7Ry
Thank you for your efforts in the review, and hope our response will address your concerns.
> On Line 200-202, Evaluation Metrics. "We use lexical matching such as F1 to match the judgments of the statements." What is lexical matching? Also, how exactly is F1 calcul... | null | null | null | null |
OneForecast: A Universal Framework for Global and Regional Weather Forecasting | Accept (poster) | Summary: This paper introduces OneForecast which leverages multiscale graph neural networks. By integrating principles from dynamical systems with multi-grid structures, OneForecast refines target regions to better capture high-frequency features and extreme events. The adaptive information propagation mechanism, equip... | Rebuttal 1:
Rebuttal: Dear Reviewer yiSD,
We are truly grateful for the time you have taken to review our paper and your insightful review. Here we response your questions point by point.
> Q1. The claim for solving over-smooth challenge needs more elaboration.
A1. Please refer to our reply A1&A2 for Reviewer z4XB.
... | Summary: Accurate weather forecasting is critical for disaster preparedness and resource management, yet traditional numerical methods are computationally intensive, and deep learning approaches often struggle with multi-scale predictions and extreme events. This paper introduces **OneForecast**, a graph neural network... | Rebuttal 1:
Rebuttal: Dear Reviewer McMn,
We are truly grateful for the time you have taken to review our paper and your insightful review. Here we response your questions point by point.
> Q1. There might be an issue with the ACC-Q700 scale in Fig 4.
A1. Thank you again for your careful review of our paper, it exac... | Summary: The paper introduces OneForecast, a universal weather forecasting framework based on GNNs. It aims to improve global-regional weather forecasting by leveraging multi-scale graph structures, adaptive information propagation mechanisms, and a neural nested grid method. The proposed framework improves forecast ac... | Rebuttal 1:
Rebuttal: Dear Reviewer z4XB,
We are truly grateful for the time you have taken to review our paper and your insightful review. Here we response your questions point by point.
> Q1&Q2. Explicitly define dynamic system modeling capability.
A1&A2: Dynamic systems modeling represents multi-scale interaction... | Summary: This paper propsoes a novel method for deep learnig based weather forecasting. The proposed method is based on graph neural networks and introduces new approaches for message passing, and for integrating high resolution and low resolution data. The proposed method outperformed exisitng method in orth short and... | Rebuttal 1:
Rebuttal: Dear Reviewer SzD8,
We are truly grateful for the time you have taken to review our paper and your insightful review. Here we response your questions point by point.
> Q1. The paper is missing comparisons with traditional numerical methods.
A1. We add the comparison with the traditional numeric... | null | null | null | null | null | null |
Learning Distribution-wise Control in Representation Space for Language Models | Accept (poster) | Summary: **Post-rebuttal edit: the authors have provided detailed responses to my concerns during the discussion phase, and seem to have taken on board my concerns about the clarity of presentation. As a result, I'm happy to increase my score from 2 to 3. The reason why I haven't gone further is what I see as an import... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback on our submission! We’re encouraged by your recognition of our experimental results and value your suggestions for improving clarity and rigor. Below, we address your concerns and outline our revision plan.
## About Main Concern: Method Clarity & Training Pr... | Summary: The authors present a new parameter efficient finetuning approach D-ReFT. Whereas ReFT learns a deterministic (peculiarly parametrized) linear transformation of activations, D-ReFT instead learns a similarly peculiarly parameterized linear transformation that is stochastic. Specifically, they replace a part of... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful and constructive review of our manuscript. We’ve noted your main concerns regarding the statistical significance of our results and the need for clarification on our ablation choices. Thus, we try to address your feedback as below:
## About eval setting & statistical... | Summary: This work deals with expanding point-wise representation engineering based interventions (ReFT) to be distribution-based ones (D-ReFT) by changing deterministic standard MLP layers to be stochastic via a reparametrization of the layer into two layers ( one for the mean and the other for the variance (+ gaussia... | Rebuttal 1:
Rebuttal: We thank you for your detailed and constructive review of our manuscript with a positive assessment of our work. We are also grateful for your specific suggestions, which we believe will significantly strengthen the paper. Specifically, we address key feedbacks below:
## About comparison with LoF... | Summary: The author suggests a generic methodology to replace deterministic interventions with distribution-level ones. Commonsense and arithmetic reasoning benchmarks on different Llama models are employed. When their method is used on early layers, the performance of tested tasks improves. When distribution-level int... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback! We sincerely appreciate the time and effort you dedicated to reviewing our paper and providing thoughtful comments. We have carefully reviewed your concerns and questions and recognize that your primary focus is **the generalization to other series models ... | null | null | null | null | null | null |
Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough? | Accept (spotlight poster) | Summary: The paper addresses the problem of choosing an appropriate number of inducing points for a sparse Gaussian process in the context of continual learning, where batches of data are observed sequentially, such that the total number of data points is not known before training, which prevents the use of heuristics ... | Rebuttal 1:
Rebuttal: Thanks for your detailed review and your clear recommendations for improvement. We appreciate your positive feedback on our work.
**Suggestions on main results presentation and is there any particular reason why you only include NLPD results in the appendix?**
Thank you for your suggestions on w... | Summary: The paper introduces a new criterion for determining the number of inducing variables in a context of continual learning with single-output GP regression models. The general idea is to automatically adjust model size while maintaining near-optimal performance, but without the need of seeing future data points.... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thorough review. We appreciate your perspective on the significance of our work.
**[Q] Focus on regression problems.**
As you noted, the main reason is the theoretical guarantees and the bound from Titsias (2014), which was derived for GP regression with Gaussian ... | Summary: In a streaming data setting, where access to previously observed batches of data is not available, one cannot use Gaussian process methods with non-degenerate (i.e., full-rank) kernels. A very popular approach is to approximate the full Gaussian process with a variational approximation, in which the posterior ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and your suggestions. We appreciate your positive feedback.
**Connection of our work with NNs**
As we note in response to other reviewers, we view VIPS as a first step towards adaptive size in more general settings. Since GPs and NNs share structural similarit... | Summary: The submission proposes a method that dynamically adjusts the model size (i.e., the number of inducing points in a sparse Gaussian process) while maintaining near-optimal performance in a continual learning setting, where data is presented as a stream and data storage is not allowed. The proposed method requir... | Rebuttal 1:
Rebuttal: Thank you for your encouraging feedback and for recognising the relevance of our work. We appreciate your positive comments on the clarity of our writing and experimental design.
**Q1: Optimizing the inducing points, rather than selecting them from the training data, may lead to better solutions... | null | null | null | null | null | null |
Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models | Accept (poster) | Summary: The paper points out the issue of feature instability in SAE training and designs the A-SAE and RA-SAE methods. These methods restrict the SAE feature Z using an archetypal dictionary D, resulting in SAEs with more stable features. The paper also introduces metrics for evaluating SAEs: (i) sparse reconstructio... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We are glad you found the stability contributions and mathematical formulation of Archetypal SAEs to be novel and well-motivated. Below we address specific comments.
---
> **On Diversity of Analyzed Pretrained Models**
We agree that evaluating across a range of model... | Summary: The authors find that current SAE architectures (ReLU, Jump-ReLU, TopK) exhibit instability: the learned concepts differ between runs, even on the same data. They measure this with a new metric:
$$\text{max}_{\Pi} \frac{1}{n} \text{Tr}(D^\intercal \Pi D')$$
Where $\Pi$ is the optimal alignment between $D$ and... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review! We appreciate your engagement. Below, we address specific comments.
---
> **Stability, why It matters, and how we measure it**
We agree that interpretability—not reconstruction or sparsity per se—is the end goal of SAEs. However, we argue stabil... | Summary: Sparse autoencoders (SAEs) are a promising unsupervised learning approach to find relevant and interpretable concepts of representations, e.g., for language or vision models. This paper argues that concepts extracted from SAEs are unstable when a fix model is trained multiple times on the same dataset or train... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed review—we sincerely appreciate the rigorous and constructive evaluation! The suggested related work and the thoughtful questions are very helpful. While not all references pertain directly to SAEs, the community we attempt to reach, the body of work highlight... | Summary: The paper proposes an extension of vanilla SAE approaches to archetypal SAE, a type of geometric anchoring that improves various shortcomings, stability and plausibility, of vanilla SAEs. Further, the authors introduce two new benchmarks for plausibility and identifiability. The paper thoroughly evaluates the ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review! We appreciate your recognition of the paper’s strengths, including the methodological clarity, the novel evaluation metrics, and the thorough empirical analysis. Below, we address specific comments.
---
> **Clarification of Dataset Usage (Se... | null | null | null | null | null | null |
Clustering via Self-Supervised Diffusion | Accept (poster) | Summary: This paper introduces Clustering via Diffusion (CLUDI), a self-supervised clustering framework that uses diffusion models on top of pre-trained Vision Transformer (ViT) features. The core idea is a teacher–student setup: a diffusion model (teacher) generates stochastic cluster assignment embeddings, while the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions.
**Embedding-matrix ablations:** Our explorations on the learned projection matrix $\bf{E}$ yielded the following results for ImageNet 100:
- Using the teacher output $\tilde{\bf{z}}_0$ directly as a target gives a drop in accuracy of around 4... | Summary: This paper introduces a novel self-supervised image clustering framework incorporating the ideas of diffusion models to achieve accurate and robust clustering. The framework is designed in a teacher-student paradigm to train a teacher model to produce diverse cluster assignments and a student model for stable ... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the comments and questions.
**Robustness:**
The experimental evidence for the robustness of our approach is presented in Figure 2, which shows that when we corrupt the ViT inputs (via feature dropout + Gaussian noise), the degradation in classification accuracy is compl... | Summary: This paper proposes Clustering via Diffusion (CLUDI), a method using diffusion models to cluster unlabeled image data. The authors take pre-trained ViT features as input, then learn a diffusion-based generative process that refines random noise into “assignment embeddings.” A classification head maps these emb... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the comments and questions.
**Number of diffusion steps at inference:**
Our experiments show that increasing the number of DDIM diffusion steps at inference reduces the variance of the accuracy across independent runs of the diffusion model. Moreover, for small latent ... | Summary: - This paper presents CLUDI, a framework that combines pre-trained Vision Transformer (ViT) features with diffusion models for clustering tasks.
- While leveraging ViT for feature extraction and using diffusion models to enhance performance might offer some improvements, the significance of this approach c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and questions.
**Non-incremental nature of the results:** The experimental results show that ViT features cannot by themselves explain the success of CLUDI, our model. CLUDI's advantage is evident in its superior test metrics across all models and datasets (... | null | null | null | null | null | null |
Robust Consensus Anchor Learning for Efficient Multi-view Subspace Clustering | Accept (poster) | Summary: This paper proposes a novel Robust Consensus anchor learning for efficient multi-view Subspace Clustering (RCSC). The authors first theoretically demonstrate that an anchor graph with block-diagonal structure can be achieved if the objective function satisfies certain conditions. The authors impose the orthogo... | Rebuttal 1:
Rebuttal: Q1: The explanation why UpA∈Rdp×1 can represent the basis matrix and the related analysis can be given here.
A1: Good question! As reviewer mentioned, we adopt A∈Rd×l to represent the unified anchors, l and d are the number of anchors and shared dimension across views, respectively. UpA∈Rdp×l rep... | Summary: To improve the scalability of the multi-view subspace clustering to large-scale data, this paper proposes Robust Consensus anchors learning for efficient multi-view Subspace Clustering (RCSC), which joints the robust anchor learning, anchor graph construction, and partition into a unified framework. This paper... | Rebuttal 1:
Rebuttal: Q1: It is important to emphasize the connection and differences between the following works: i.e., FPMVS-CAG and SMVSC, especially SMVSC.
A1: Good question! FPMVS-CAG jointly performs anchor selection and subspace graph construction into a framework. Then the two processes can be negotiated with ... | Summary: This paper proposes Robust Consensus anchors learning for efficient multi-view Subspace Clustering (RCSC). The authors first show that if the data are sufficiently sampled from independent subspaces, and the objective function meets some conditions, the achieved anchor graph has the block-diagonal structure. A... | Rebuttal 1:
Rebuttal: Q1: The reason why the partition can be integrated into the unified framework.
A1: Thanks for the comment! To integrate the partition into the unified framework, we adopt the orthogonal and nonnegative factorization to directly assign clusters to the data. The reason why we integrate the partitio... | Summary: This study proposes a novel method named RCSC, which aims to improve the clustering effectiveness on multi-view datasets by jointly addressing anchor graph construction, partitioning, and robust anchor learning. A key finding is that when data are adequately sampled from independent subspaces and the objective... | Rebuttal 1:
Rebuttal: Q1: Streamline and differentiate some details in the abstract.
A1: Thanks for the comment! It is needed to streamline and differentiate some details in the abstract, which is able to improve the overall readability and appeal of the manuscript. We will remove the related details regarding the sig... | null | null | null | null | null | null |
LotteryCodec: Searching the Implicit Representation in a Random Network for Low-Complexity Image Compression | Accept (spotlight poster) | Summary: This paper investigate the lottery ticket hypothesis for implicit representation based image compression.
It proposes to overfits a binary mask and modulation vectors to the source image, and then leverages a randomly initialized neural network to generate the reconstruction.
The proposed LotteryCodec ac... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s valuable comments and for highlighting Choi’s ICML 2023 paper. Our detailed responses to each comment are as follows:
* (1). Following the suggestion, we have conducted additional MS-SSIM experiments on the Kodak dataset. The results, presented in **Table 4.1**, de... | Summary: This paper introduces LotteryCodec, a novel, low-complexity image compression scheme based on overfitting. LotteryCodec effectively overfits a binary mask of an over-parameterized, randomly initialized network to an image, achieving high-performance compression. To enhance its performance, techniques such as F... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's valuable comments. Our responses to the reviewer's main concerns are as follows:
* Encoding/decoding complexity. Practical encoding/decoding time and peak memory usage across images with various resolutions are reported in **Table 2.1** (see our response to **Reviewer... | Summary: The paper presents LotteryCodec, a new method for single-image compression that builds on the idea that large, randomly initialized neural networks contain subnetworks capable of matching the performance of fully trained networks. Concretely, instead of training and transmitting all synthesis network parameter... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments. We first respond to the reviewer's main concerns:
* W1: Proof of Lottery Codec Hypothesis (LCH). Although a rigorous bound supporting the LCH is not available, we can provide a rough validation based on existing proofs for the Strong Lottery Tickets Hyp... | Summary: The paper introduces the Lottery Codec hypothesis based on the Lottery Ticket hypothesis and implements an image codec, LotteryCodec, which achieves strong performance and outperforms the best INR-based image codec while maintaining low complexity.
Claims And Evidence: Some claims are not clearly elaborated: ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable comments and recommending two interesting papers. We will add (a) proper discussions of both papers, and (b) suggested ablation studies and tables to the revised manuscript.
For a discussion of Choi et al. (2023), please refer to our response to Reviewer [yn... | null | null | null | null | null | null |
SEAD: Unsupervised Ensemble of Streaming Anomaly Detectors | Accept (poster) | Summary: The paper proposes SEAD, an unsupervised ensemble method for streaming anomaly detection (AD) that dynamically selects the best base detectors without labeled data. It leverages multiplicative weights updates to adjust model weights based on normalized anomaly scores and introduces SEAD++ for runtime optimizat... | Rebuttal 1:
Rebuttal: We thank the reviewer for valuable comments and suggestions. We propose to add these to the final camera ready version.
> On hyper-parameter ablations
We add the following experiment to compare against the choice of $\eta = [1, 0.1, 0.01]$ and $\lambda = [10^{-2}, 10^{-4}, 10^{-6}]$. This is in... | Summary: The authors study unsupervised anomaly detection on data streams, where data distribution can change over time, affecting single model performance. The authors introduce a weighted ensemble that combine individual anomaly detectors based on how low their normalized scores are. The method is tested on 15 datase... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and address the main concern raised, namely that of the variance.
> The reported variances seem very large. Not clear why this happens for SEAD, but this raises questions about the stability of the method.
As mentioned in the paper, SEAD has the *lowest* ... | Summary: This paper proposes streaming ensemble of anomaly detectors, a model selection algorithm for streaming, unsupervised AD. The key insight that SEAD leverages is that anomalies by definition are ‘rare’, which SEAD uses to work in a fully unsupervised fashion. SEAD sets the weights for the individual models and c... | Rebuttal 1:
Rebuttal: > How is SEAD adaptive to distribution changes
SEAD updates the weigths of the base detectors using the multiplicative weight updates (MWU). In the learning theory literature, it has been established that when the parameters of learning rate and regularization strength are appropriately chosen, ... | Summary: The paper introduces SEAD (Streaming Ensemble of Anomaly Detectors), an unsupervised, online model selection algorithm for anomaly detection in streaming data, where labels are unavailable, and data distributions change over time. SEAD dynamically assigns weights to multiple anomaly detection models using Mult... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable questions and comments. Our responses to the raised points are outlined below.
> Limitations of SEAD: The performance can only be as good as the best detector
SEAD is the first online, unsupervised model-selection algorithm for anomaly detection (AD). T... | null | null | null | null | null | null |
An End-to-End Model for Logits-Based Large Language Models Watermarking | Accept (poster) | Summary: This paper proposes an end-to-end model for logits-based LLM watermarking. The model consists of a logit perturbation generation network and a watermark detection network, and these two networks are trained in an end-to-end pipeline. To improve the robustness of watermark, a LLM is used to paraphrase watermark... | Rebuttal 1:
Rebuttal: Dear Reviewer eHFF,
We sincerely appreciate the time and effort you have dedicated. Below, we summarize the key responses to your concerns.
# Source of Robustness
We argue that robustness arises from two factors:
1.**Watermark Decoder**: We compare our neural decoder (ND) with a statistical d... | Summary: The paper introduces a logits-based end-to-end model for watermarking LLM generated text. As the existing method can not achieve an optimal balance between text quality and robustness, the authors propose a novel approach that jointly optimizes encoder and decoder to improve both text quality and robustness. E... | Rebuttal 1:
Rebuttal: Dear Reviewer bhtZ,
We sincerely appreciate the time and effort you have dedicated to our manuscript. Below, we summarize the key responses to your concerns.
# Case Study
We have included three pairs of non-watermarked and watermarked samples generated from the same prompt in Appendix E (see ... | Summary: The authors propose a method to enhance the robustness of logit-based watermarking techniques while preserving text quality. The main idea is to use a model to generate the "biases" for the logits.
Claims And Evidence: The claims are generally supported by enough evidence.
Methods And Evaluation Criteria: Th... | Rebuttal 1:
Rebuttal: Dear Reviewer sPDj,
We sincerely appreciate the time and effort you have dedicated to our manuscript. Below, we summarize the key responses to your concerns.
# Theoretical Guarantees
||1%FPR TPR↑ (CL / SS / CP / PA)|Best F1↑ (CL / SS / CP / PA)|
|-------------------|---------------------------... | null | null | null | null | null | null | null | null |
Phase and Amplitude-aware Prompting for Enhancing Adversarial Robustness | Accept (poster) | Summary: This paper shows another defense based on visual prompting. They extended previous defenses applying the Fourier transform and integrating this in their defense design.
This method can outperform previous methods and showed its effectiveness against adaptive attacks.
The limitations could have been better dis... | Rebuttal 1:
Rebuttal: Thanks for your great efforts spending on reviewing our paper. Your comments are important to the improvement of our work, and we address them as follows:
**The clarity on limitations.** We are sorry for the lack of clarity on limitations. The main limitation of our method is the sacrifice of nat... | Summary: This work exploits a prompt-based defense using specific texture and structure patterns, and proposes to incorporate these prompts with appropriate prompting weights according to their effects on robustness, which enhances the robustness in various scenarios with superior transferability across various network... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. The responses to your concerns are as follows:
**The superiority of our method.** Our method indeed does not outperform “Freq” by a large margin in a few scenarios. However, as shown in the Table 3, 4, 6, 7, 12 and 13, Freq sacrifices natural accuracy by a large... | Summary: This work proposes a prompting method for defense, through training prompts for each class using specific semantic patterns including structures and textures based on the Fourier Transform, which successfully defends against various general and adaptive attacks.
## Update After Rebuttal
The authors have addr... | Rebuttal 1:
Rebuttal: Thanks for your constructive suggestions. Your comments are important to our work, and we address them as follows:
**Stability in natural accuracy.** Our method lose a few natural accuracy when performing defenses. However, as shown in the Section 4, baselines lose more natural accuracy under var... | Summary: This paper proposes a defense strategy based on prompting on structures and textures, with appropriate weights adjusted by their influences on robustness for incorporating their benefit for defenses. It achieves superior defense performances on general and adaptive attacks and defense transferability.
Claims ... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and constructive suggestions. The responses to your concerns are as follows:
**Discussions on the trade-off problem.** Our method has a trade-off problem under different hyper-parameters. On naturally pre-trained models, when λ1 increases, the natural accuracy in... | null | null | null | null | null | null |
Lightweight Dataset Pruning without Full Training via Example Difficulty and Prediction Uncertainty | Accept (poster) | Summary: This paper aims to pruning datasets in the early stages of training, without the need to train on the entire dataset. To achieve this, the authors propose a new scoring metric - the DUAL Score, which simultaneously considers sample difficulty and prediction uncertainty. To address potential sample distribution... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their valuable and constructive feedback. Below, we address the concerns raised.
# Comparison with Dynamic Data Pruning Methods
Thank you for pointing out the relevant references, especially [1, 2]. To address your concerns, we conducted several experiments co... | Summary: This paper presents a dataset pruning method designed to reduce the computational burden of the pruning process. The authors introduce a strategy that leverages both difficulty and prediction uncertainty to efficiently select a coreset at an early stage of training. The effectiveness of the approach is validat... | Rebuttal 1:
Rebuttal: We appreciate your time and insightful comments. Below, we address the concerns and clarify any confusion raised.
# 1. Figures 2 & 3
First, we apologize for any confusion in Figures 2 and 3. Revised figures are available [here](https://vo.la/msMJZE) (see Figure2_revised, Figure3_revised). In bot... | Summary: - This paper introduces a new method Difficulty and Uncertainty Aware Lightweight (DUAL) that combines Dyn-Unc with a measure of prediction confidence over training.
- The authors further introduce pruning-ratio-adaptive Beta sampling, which boosts performance at all pruning ratios and particularly helps at ve... | Rebuttal 1:
Rebuttal: Thank you for your constructive review and insightful suggestion.
Before we address your concerns regarding our beta sampling method, we would like to emphasize the novelty of the DUAL score lies in its time efficiency. Many existing pruning techniques require full training to estimate example di... | Summary: This paper proposed a dataset pruning score named as Difficulty and Uncertainty-Aware Lightweight (DUAL). The main idea is two fold. First, it combines the data difficulty and data uncertainty into one numerical measure for pruning. This extends existing work on uncertainty based data pruning such as Dyn-Unc (... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful and valuable feedback. We address the given concerns and questions below.
### **1. Experiment with a more challenging dataset**
Thank you for your suggestion. Due to time constraints, we experimented on a randomly sampled 20% subset of iNaturalist 2017 on ... | null | null | null | null | null | null |
ProDiff: Prototype-Guided Diffusion for Minimal Information Trajectory Imputation | Accept (poster) | Summary: The paper proposes ProDiff, a prototype-guided diffusion model for trajectory imputation using only two endpoints as minimal information. It integrates prototype learning to embed human movement patterns and employs a denoising diffusion probabilistic model to reconstruct missing spatiotemporal data. A joint l... | Rebuttal 1:
Rebuttal: **W1**: Thank you for your insightful comment. We agree the original lacked theoretical grounding and now present a concise framework supporting prototype-based modeling of macro-level human movement.
### Theoretical Justification
Prototype learning combines clustering and contrastive learning. ... | Summary: This paper studied the task of trajectory imputation and propose ProDiff as a trajectory imputation framework that uses only two endpoints as minimal information, in order to improve previous approaches which place significant demands on data acquisition and overlook the potential of large-scale human trajecto... | Rebuttal 1:
Rebuttal: **W1：The reviewer suggested discussing how the proposed method could better leverage intermediate trajectory points when available, as they may provide valuable information for imputation beyond using only endpoints.**
Thank you for this insightful comment. We fully agree that leveraging interm... | Summary: In this work, the authors design ProDiff, a diffusion-based model for spatial data imputation. The research direction is interesting and the problem is practical, given various noises of real-world data. ProDiff consists of two components, prototype learning and a denoising diffusion probabilistic model. With ... | Rebuttal 1:
Rebuttal: **W1: In the first paragraph, there are probably more primary sources of location data.**
Thank you for pointing this out. We agree that our original manuscript could more comprehensively reflect the sources of trajectory data. In the revised manuscript, we will clarify this by explicitly adding ... | null | null | null | null | null | null | null | null |
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design | Accept (poster) | Summary: The paper introduces 'Reward-Guided Evolutionary Refinement in Diffusion models (RERD)', a framework for optimizing reward functions during inference time in diffusion models. RERD employs an iterative refinement process consisting of two key steps per iteration: noising and reward-guided denoising. This appro... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions and insightful comments! Following a reviewer's suggestion, we added (1) more ablation studies and (2) additional experiments for image generation with Stable Diffusion and MaskGiT
> Ablation studies on the noising faction (K)
Thank you for the though... | Summary: The authors introduce a novel inference-time framework for the iterative refinement and reward optimization of diffusion models. Their proposed method, Reward-Guided Evolutionary Refinement in Diffusion models (RERD), is based on the iterative refinement of generation with reward-guides denoising, and provide ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive feedback. Below are our responses to your questions:
Q: Do we need 100∗(S−1) steps?
You're absolutely right. When setting K/T=10%, and T=1000, we would indeed require 100 steps. However, this component can be adjusted in practice by reducing T or K, which of... | Summary: The paper presents a novel framework for inference time reward optimization in diffusion models, introducing an iterative refinement approach that alternates between noising and reward guided denoising steps. This method departs from conventional single shot reward optimization, aiming to iteratively refine ge... | Rebuttal 1:
Rebuttal: Thank you very much for the positive and very detailed feedback! Below we address the key questions and comments you raised:
> Q. Abolition study
Thank you for the thoughtful suggestions regarding the ablations. In response, we have conducted additional ablation studies by varying key hyperpara... | null | null | null | null | null | null | null | null |
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