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SafeAuto: Knowledge-Enhanced Safe Autonomous Driving with Multimodal Foundation Models | Accept (poster) | Summary: This paper presents SafeAuto, an MLLM-based autonomous driving system. SafeAuto has three major innovations. First, it uses a new Position-Dependent CE loss (PDCE) loss, which supervises the predicted number tokens based on their numerical difference from the ground-truth number. Second, it has a knowledge-enh... | Rebuttal 1:
Rebuttal: We extend our sincere gratitude to the reviewer for their meticulous and constructive feedback. Their insightful observations and valuable recommendations have greatly contributed to improving the rigor and clarity of our work!
> **Q1: The motivation for the PDCE loss is that using the MSE loss o... | Summary: This paper proposes SafeAuto, a novel framework for autonomous driving using multimodal foundation models. It addresses the challenges of integrating high-level reasoning and low-level control.
The main algorithm ideas include three key components. First, the Position-Dependent Cross-Entropy (PDCE) loss funct... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive feedback, and for recognizing the value of our work! The insightful suggestions and detailed comments provided have substantially contributed to enhancing the quality of our work!
> **Q1: The PDCE is the weighted sum of the KL ... | Summary: This paper proposes SafeAuto, a novel framework that enhances MLLM-based autonomous driving systems by incorporating both unstructured and structured knowledge. The model can predict high-level and low-level action prediction.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes. However, the propo... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewer for their thorough and insightful feedback. Their expertise and dedicated time have significantly contributed to improving the quality of this work!
> **Q1: The experiments are only conducted on the BDD-X/DriveLM datasets and may only be compared with some ... | Summary: SafeAuto proposes a unified framework to enhance autonomous driving systems by leveraging multimodal foundation models. It integrates three core components:
Position-Dependent Cross-Entropy (PDCE) Loss: An adaptation of the standard cross-entropy loss that incorporates digit-level proximity and place-level we... | Rebuttal 1:
Rebuttal: We are deeply grateful to the reviewer for their insightful and thorough feedback, and we appreciate the recognition of our work's contribution! The suggestions and comments made for our work have significantly helped to improve its quality.
> **Q1: Could the authors provide more details on the i... | null | null | null | null | null | null |
Flow Matching for Few-Trial Neural Adaptation with Stable Latent Dynamics | Accept (poster) | Summary: Neural representational drift is a well-known problem in brain computer interfaces (BCIs) that make it difficult to re-use a decoder over multiple days without an additional recalibration step. Importantly, the amount of data available each day for recalibration is often small. Prior work has explored ways of ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful review and recognition of our work. Below, we provide a point-by-point reply to your concerns. Due to the limit, all figures prefixed with 'R' below are available in the external link [https://drive.google.com/file/d/129vv370SF4RLanLj92-lzh_vMmkeDCve/view?pl... | Summary: This paper proposes Flow-based Distributional Alignment (FDA), a few-shot alignment or adaptation method for neural signals across days, using flow matching. While neural activity adaptation methods in general struggle to maintain stable performance across multiple days, the authors claim that FDA would perfor... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful review and recognition of our work. Below, we provide a point-by-point reply to your concerns.
- Additional comparison with NDT-2
We conducted an additional comparison against NDT-2 by pre-training it on a single session with supervised readout training an... | Summary: The work propose to utilize Flow-based distribution alignment (FDA) to learn flexible neural representations with stable latent dynamics, and performs source free alignment with likelihood maximization. The author additionally performed theoretical analysis on the stability of latent dynamics, and performed ex... | Rebuttal 1:
Rebuttal: We sincerely thank you for your careful review and recognition of our work. Below, we provide a point-by-point reply to your concerns. Due to the limit, all figures prefixed with 'R' below are available in the external link[https://drive.google.com/file/d/129vv370SF4RLanLj92-lzh_vMmkeDCve/view?pli... | Summary: The paper introduces a new approach for learning and aligning neural representations across multiple sessions to link neural activity with behavioral actions. Particularly, the authors present a neural decoder that aligns recordings from different sessions (e.g., over multiple days)
based on flow matching in... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and recognition of our work. Below we provide a detailed response to your concerns, with reference figures in the supplementary material [https://drive.google.com/file/d/129vv370SF4RLanLj92-lzh_vMmkeDCve/view?pli=1], indicated by Fig.R.
### Weaknesses... | null | null | null | null | null | null |
Improved Convex Decomposition with Ensembling and Boolean Primitives | Reject | Summary: This paper proposes a novel method for representing scenes using convex primitives enhanced with a Boolean (set-difference) operation. In contrast to prior work that uses a fixed number of primitives, the authors introduce an ensembling strategy to select an adaptive number of positive and negative primitives ... | Rebuttal 1:
Rebuttal: Thanks for reading our paper and offering positive feedback.
## 8. Cost of Ensembling
Please see __Tables 1 and 3__ for detailed timing breakdowns. Individual models we trained require betewen 0.84 to 2.06 seconds. This is over an order of magnitude faster than prior work (40 seconds), while simu... | Summary: This paper aims to decompose a scene into different primitives. Based on the work "Convex Decomposition of Indoor Scenes"[1], this paper introduces two strategy to improve the baseline: (1) Introducing the negative primitives for the decomposition; (2) ensembling multiple networks' results and choose the best.... | Rebuttal 1:
Rebuttal: Thanks for taking the time to look at our work.
## 7. Value of Negative Primitives
You make a great point - all methods produce good results on average. We don’t claim that quality keeps improving as we increase negative primitives beyond a point. Instead, our aim is to show it’s possible to fit C... | Summary: This paper addresses the problem of parsing complex 3D scenes into geometric primitives, focusing on improving accuracy by incorporating boolean operations (set differencing via negative primitives) and ensembling to dynamically select the number of primitives per scene. The authors propose a hybrid approach c... | Rebuttal 1:
Rebuttal: Thanks for reviewing our paper.
## 4. Theoretical Analysis
Multiple reviewers expressed interest in theoretical justification as to why boolean primitives are advantageous in fitting complex real-world scenes. We provided qualitative evidence in __Fig. 3__, in which we model a cube with a hole p... | Summary: This paper aims for the task of fitting a scene with simple primitives. To address the challenges of local minima, poor representing complex structure and highly relying on good initialization, authors propose a novel negative primitive design. Experiments on NYUv2 and ALION show advantages of this method.
Cl... | Rebuttal 1:
Rebuttal: Thanks for the feedback on our paper.
## 1. Global Comment
We'd like to refresh the reviewers with a summary of our contributions:
1. We depart from the limited NYUv2 dataset used in existing primitive-fitting papers and show how to make primitive-fitting work on real-world natural images via a... | null | null | null | null | null | null |
TextCenGen: Attention-Guided Text-Centric Background Adaptation for Text-to-Image Generation | Accept (poster) | Summary: This paper mainly targets text-to-image generation. The authors focus on an interesting problem: after generating images, one would potentially want to insert specific visual texts into the images, and it would be better if the area to be inserted has clean background rather than being occupied with other obje... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We address each point below.
### [W1]Task Importance Placement in Introduction
We'll add to the introduction: "As shown in Fig. 6, creating text-friendly images is essential for graphic design applications (validated by our 114-participant user study)."
### [... | Summary: This paper introduces TextCenGen, a training-free method for generating text-friendly images. While traditional text-to-image (T2I) models can create high-quality images, they typically don't account for the need to reserve space for text placement. TextCenGen addresses this challenge through several innovatio... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We address your concerns with additional experiments and analysis below.
### [Q1,Q3]Multi-Object and Complex Scene Handling
Our approach shows strong performance in complex scenes with multiple objects. We additionally evaluated on the Desigen dataset, which i... | Summary: This paper aims to solve the problem of generating a background image conditioning on intended size and position of overlaying texts. The proposed approach, TextCenGen, is to generate a regular image and a background image at the same time, and use the text intended region and attention map from the regular im... | Rebuttal 1:
Rebuttal: Thank you for your valuable review.
### Evaluation on Poster/Ads Datasets
We selected the Desigen dataset as a layout design dataset for evaluation. We successfully downloaded 53,577 usable images, with 52,806 used for training the Desigen version. The remaining 771 images from the validation se... | null | null | null | null | null | null | null | null |
GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing | Accept (poster) | Summary: The paper introduces GeoPixel, a remote sensing multimodal LLM for pixel level understanding and reasoning in high resolution aerial images. The authors present GeoPixelD, a new dataset with detailed, spatially-aware annotations for grounded conversations in remote sensing. The authors develop an adaptive imag... | Rebuttal 1:
Rebuttal: Dear Reviewer fxwn,
Thank you for your comprehensive review of our submission. We appreciate your insights and the opportunity to clarify and expand upon aspects of our work.
**Performance Benchmarking:** We appreciate your acknowledgment of GeoPixel's superior performance in handling high-r... | Summary: This work introduces a multi-modal Grounded Conversation Generation (GCG) dataset that includes grounded descriptions for high-resolution remote sensing images, along with a benchmark featuring human-verified annotations. By leveraging recent advances in large vision-language models, the authors propose a mode... | Rebuttal 1:
Rebuttal: Dear Reviewer Vj3H,
Thank you for your thorough review and insightful comments regarding our submission. We appreciate the opportunity to address the issues raised and clarify aspects of our research methodology and dataset.
**Method Novelty and Framework Comparison:** While our method frame... | Summary: This paper introduces GeoPixel and GeoPixelD.
GeoPixel is a combination of models that receives high-res RGB satellite imagery and outputs text (e.g., a description in natural language) and a dense segmentation map. The combination consists of a frozen vision encoder that "tokenizes" the image and feeds thes... | Rebuttal 1:
Rebuttal: Dear Reviewer zikq,
Thank you for your detailed review and insights on our submission, GeoPixel. We appreciate the opportunity to address your concerns and clarify aspects of our research.
**Evaluation on Other RS Image-to-Text Datasets:** You raised an important question regarding why GeoPi... | Summary: The paper presents GeoPixel, a novel large multimodal model (LMM) that advances high-resolution remote sensing (RS) image analysis by integrating pixel-level grounding with textual understanding, addressing limitations in existing RS-LMMs. Its architecture features an adaptive image divider for processing 4K-r... | Rebuttal 1:
Rebuttal: Dear Reviewer Amru,
Thank you for your detailed review and constructive comments regarding our submission on GeoPixel. We appreciate your taking the time to analyze our work and the insights you provided. Below, we address your comments and concerns:
**Clarity on Adapted Baselines:** In our ... | null | null | null | null | null | null |
Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures | Accept (poster) | Summary: This paper considers the maximum coverage problem, where there is a universe of $n$ elements and we are presented with $d$ subsets of these $n$ elements. The goal is to retain and output a set of $k$ subsets (where $k$ is a parameter given in advance) whose union covers as many items as possible. The exact ver... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and encouraging comments. We address them below.
Here is a reference showing that a 1-1/e approximation (unless P = NP) in polynomial time is tight [1]. We will be sure to include this citation in the next version of the paper.
We will also be sure to inclu... | Summary: The paper considers the maximum coverage problem in the turnstile model.
The offline problem considers $d$ subsets from a universe $[n]$, and the goal is to output $k < d$ subsets such that the union of the sets contains the largest possible number of items from $[n]$.
This can also be expressed in matrix nota... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review and comments. We address the comments and questions below.
We will be sure to scale and format the figures more appropriately in the next version of the paper.
Concerning the $O(\log n)$ factors in Theorem 1.1: Yes both Bateni et al. (2017) and M... | Summary: The paper introduces a linear sketch for the maximum coverage problem that supports both insertions and deletions of item-feature pairs under the turnstile streaming model. This sketch improves on previous work that considers insertion-only streams, or which support only the insertion or deletion of entire sub... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough and encouraging review. We address their comments and questions below.
Regarding the experimental evaluation:
We chose Gulyas et al. (2016) as the baseline primarily because it represents the standard offline approach for the fingerprinting application, ... | Summary: This paper studies the maximum coverage problem in the data stream model and give the first turnstile algorithm, i.e., allowing both insertion and deletion, for this problem with space complexity almost match previous insertion-only streaming algorithms.
Claims And Evidence: Yes, the claims made in the submis... | Rebuttal 1:
Rebuttal: We thank the reviewer for their review and encouraging comments. We will be sure to adjust the writing, in particular the introduction, to make it more machine learning oriented in the next version of the paper. For example, we will expand on the applications to sensor placement, influence maximiz... | null | null | null | null | null | null |
The Best of Both Worlds: Bridging Quality and Diversity in Data Selection with Bipartite Graph | Accept (poster) | Summary: This paper proposes a novel method for selection SFT data for LLM fine-tuning. The proposed GraphFilter method pairs each instruction in the SFT dataset with a corresponding set of n-grams. It then assigns a priority rank to each example using a priority function that takes into account both quality and divers... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and thoughtful comments. Below, we address the specific points raised:
## 1. Quality Metric
We are sorry about the confusion caused by this description. Equation 2 essentially measures the difficulty of the example, as suggested by [1, 2]. The ... | Summary: The paper presents GraphFilter, a data selection approach designed to balance quality and diversity in SFT for LLMs. The key contribution is formulating data selection as a set cover problem and leveraging a bipartite graph structure where sentences are connected to their constituent n-grams. The priority func... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback of GraphFilter. We address your comments and questions below.
## 1. Additional Diversity-Based Methods
We appreciate the reviewer's suggestion to explore additional clustering-based approaches for diversity. Following this recommendation, we co... | Summary: The paper introduces GRAPHFILTER, a data selection method for LLM fine-tuning that balances quality and diversity by modeling the dataset as a bipartite graph of sentences and n-grams. The approach iteratively selects sentences using a priority function combining SUPERFILTER (quality) and TF-IDF (diversity). E... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough assessment.
## 1. Quality Metrics
We would like to clarify that **GraphFilter is designed to be agnostic to the specific quality metric used.** We discovered an inaccuracy in the original Table 4 results. The updated results presented below actually strengt... | Summary: This paper introduces GRAPHFILTER, a method to optimize data selection for training large language models by balancing quality and diversity. Using a bipartite graph and a priority function, it enhances model performance and efficiency. Extensive tests show that GRAPHFILTER surpasses traditional methods, demon... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review of our paper.
## 1. Diversity Approach
We would like to highlight that **our research demonstrates that lexical diversity through n-grams serves as an effective proxy for semantic diversity.** As shown in Figure 2a and 2c, we demonstrated that the sub... | Summary: This paper presents GRAPHFILTER, a novel data selection method designed to address the challenge of balancing data quality and diversity in SFT. The core idea is to model the dataset as a bipartite graph where sentences are connected to their constituent n-grams. By using a priority function that multiplicativ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review of our paper on GraphFilter.
## 1. Generalizability
GraphFilter demonstrates strong generalizability through consistent performance across three model backbones and six diverse benchmarks. The Magpie dataset used in our experiments is a general datase... | null | null | null | null |
SageAttention2: Efficient Attention with Thorough Outlier Smoothing and Per-thread INT4 Quantization | Accept (poster) | Summary: In Transformer-based models, SageAttention accelerates self-attention through quantization, but its use of INT8 for queries and keys is slower than INT4. Moreover, its acceleration is limited to specific Nvidia architectures due to FP16 computations. To address this issue, this paper proposes a thread-level gr... | Rebuttal 1:
Rebuttal: Dear Reviewer aDo7,
Thank you for your valuable suggestions and questions.
---
>### Weakness1
**Reply**: We appreciate the valuable question. We argue that:
1. We first discover the critical role of accumulator precision in PV matrix multiplication for Attention; this is an essential insight ... | Summary: The authors propose SageAttention2, where they manage to quantize key and query matrices in the attention computation to INT4 while the softmax outputs and value matrices are quantized to FP8. They show that the quality degradation is managable in this configuration. Meanwhile, if the key and query matrices ar... | Rebuttal 1:
Rebuttal: Dear Reviewer r8J3,
Thank you for your valuable question. Below, we address the question raised.
---
>**Question1.** Do the authors think that it is possible for PV computation to be quantized to sub-8-precision as well?
**Reply**:
Thank you for the insightful suggestion. The answer is yes - ... | Summary: This paper makes the attention computation more efficient. It uses INT4 quantization of Q and K, instead of INT8 quantization. To enhance the accuracy of INT4, this paper proposes a outlier smoothing strategy, which is well-motivated. The overall design and implementation take hardware characteristics into acc... | Rebuttal 1:
Rebuttal: Dear Reviewer t1YD,
Thank you for your valuable suggestions and questions. Below, we address each point raised.
---
>**Weakness1.** The effectiveness of proposed Q/K smoothing is evaluated empirically. The evaluation is sufficient but it might be better to analysis the theoretical benefits of Q... | Summary: This paper proposed several improvements on SageAttention to make it comparable with FlashAttention 3 in terms of speed but better in accuracy. The enhancements mainly focused on enabling lower precision compared to previous SageAttention, i.e. move from INT8 to INT4 for Q*K^T and from FP16 to FP8 for P*V. To ... | Rebuttal 1:
Rebuttal: Dear Reviewer KKjK,
Thank you for your valuable suggestions and questions. Below, we address each point raised.
---
>**W1.** Author highlighted several times about the comparison with FlashAttention2/3 throughout the paper, however, FlashAttn were missing in many cases in the main experimental ... | Summary: SageAttention2 introduces a new way to enhance the accuracy and efficiency of attention through a 3 pronged process: firstly, it introduces an INT4 matrix-multiplication technique for query-key and FP8 matmul technique for attention weight and values; secondly, it proposes a smoothing technique for queries to ... | Rebuttal 1:
Rebuttal: Dear Reviewer 1zQu,
Thank you for your valuable suggestions and questions. Below, we address each point raised.
---
>### Comment1
**Reply**: Thank you for your valuable suggestion. We compared the accuracy of sageattention, sage2-8b, sage2-4b, and sage2-4b without smooth Q across CogVideo laye... | null | null | null | null |
Hi-Patch: Hierarchical Patch GNN for Irregular Multivariate Time Series | Accept (poster) | Summary: The paper introduces Hi-Patch, a hierarchical patch graph network designed for IMTS, where variables have different sampling rates. Hi-Patch models both local and global dependencies across different scales. It represents observations as nodes, captures short-term dependencies using intra-patch graphs, and pro... | Rebuttal 1:
Rebuttal: # Responses to Reviewer Ko9v
**Q1. The motivation and experimental validation of IMTS multi-scale modeling.**
**A1.**
1. **Motivation and Significance of Multi-scale Information**
Our work is grounded in the premise that multi-scale information is essential for general time series analysis... | Summary: The paper introduces a graph-based framework called **Hi-Patch** to handle irregularly sampled multivariate time series. The approach divides the time axis into patches of short intervals, capturing local (fine-grained) temporal patterns for densely sampled variables in each patch. It then progressively aggreg... | Rebuttal 1:
Rebuttal: # Responses to Reviewer 2e2R
**W1 & Q1. The increased complexity of the hierarchical design may affect scalability in massive datasets. How does it scale computationally when applied to highly long time series or datasets with significantly higher sampling densities?**
**A1.**
1. **Complexity i... | Summary: This paper introduces a Hierarchical Patch Graph Neural Network (Hi-Patch) for Irregular Multivariate Time Series (IMTS) modeling, where variables have distinct sampling rates and exhibit multi-scale dependencies. Existing multi-scale analysis methods struggle with IMTS due to their assumption of regular sampl... | Rebuttal 1:
Rebuttal: # Responses to Reviewer ZR6y
**W1. Hi-Patch appears computationally expensive, which may limit its scalability.**
**A1.** We address this concern from both sparse and dense data perspectives.
1. **Trade-off in Sparse IMTS**
As detailed in Appendix G.1, the primary computational cost of our ... | null | null | null | null | null | null | null | null |
Hierarchical Equivariant Policy via Frame Transfer | Accept (poster) | Summary: Hierarchical Equivariant Policy (HEP) enhances hierarchical policy learning by introducing a frame transfer interface, where the high-level agent’s output serves as a coordinate frame for the low-level agent, improving flexibility and inductive bias. It also integrates domain symmetries at both levels, ensurin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their response. We respond below:
---
> “...The authors should provide more explanation to clarify the novelty of their approach.”
We agree with the reviewer that there exist various works on hierarchical policy learning. However, our method is the first to incorporate ... | Summary: The paper proposes to develop SO(2)xT(3) equivariant hierarchical policy for imitation learning learnt policy. The high-level proposes the translation target in the canonical world frame. While the low-level policy uses the local frame to diffuse the action. The equivariance is achieved via frame transfer inte... | Rebuttal 1:
Rebuttal: We thank the reviewer for their response. We respond below:
> "...related baselines are missing like equivariant policies, e.g., Equivariant Diffusion Policy. I would like understand the difference of having a hierarchical policy and more simple versions"
> "There should be work on equivariant p... | Summary: The paper proposes a novel Hierarchical Equivariant Policy (HEP) framework for robotic manipulation tasks, combining hierarchical learning with translation and rotation equivariance via a flexible Frame Transfer interface. HEP decomposes tasks into high-level coarse predictions and low-level fine-grained traje... | Rebuttal 1:
Rebuttal: We thank the reviewer for their response. We respond below:
> “The theoretical claims seem correct under the assumptions stated, although they are impossible to verify due to the deep neural net.”
We thank the reviewer for raising this important point. While our theoretical claims appear valid un... | Summary: The paper introduces Hierarchical Equivariant Policy (HEP), a framework for hierarchical reinforcement learning that integrates equivariance (geometric symmetry) into a two-level policy architecture. The high-level policy outputs a coarse 3D subgoal (keypose), essentially a translation in space representing th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We respond below:
---
> "HEP addresses translational equivariance thoroughly... currently, the framework isn’t equipped to specify or leverage rotations... could limit performance on tasks where orientation is critical."
Thank you for bringi... | null | null | null | null | null | null |
GHOST: Generalizable One-Shot Federated Graph Learning with Proxy-Based Topology Knowledge Retention | Accept (poster) | Summary: This paper proposes GHOST, a novel generalized one-shot FGL framework designed to address challenges related to generalizable capability and catastrophic forgetting. The method involves two main components: Dual-Level Aligned Proxy Model and Topology-Conscious Knowledge Retention. In the first component, each ... | Rebuttal 1:
Rebuttal: ***Dear Reviewer TQDx:***
We greatly appreciate your positive feedback on our work, as well as the thoughtful concerns and questions you raised. We have carefully considered each of your comments and provided detailed responses.
**Weakness:**
**W1: Implementation details for applying one-shot F... | Summary: This work focuses on issues of communication overload, generalizability and catastrophic forgetting and proposes a one-shot approach where each client constructs a proxy model After the alignment, these models are then uploaded to the server to train a global model in an ensemble manner. This work also adopts ... | Rebuttal 1:
Rebuttal: ***Dear Reviewer 9vCa:***
We sincerely appreciate the time and effort you have dedicated to reviewing our paper. We hope that the detailed responses provided below will effectively address your concerns and offer the necessary clarifications.
**Weakness：**
**W1: Lack of further explanation of t... | Summary: This study tackles challenges such as communication overhead, limited generalization ability, and catastrophic forgetting. It introduces a one-shot strategy where clients independently build proxy models, which are later aligned in both feature-structural level and aggregated on the server to form a global mod... | Rebuttal 1:
Rebuttal: ***Dear Reviewer AgVq:***
Thank you for your thoughtful review and for highlighting important concerns. Below, we provide detailed responses to clarify our proposed approach.
**Weaknesses & Questions：**
**W1: Notation clarity and summarization.**
We sincerely appreciate the reviewer’s valuabl... | null | null | null | null | null | null | null | null |
Adaptive Data Collection for Robust Learning Across Multiple Distributions | Accept (poster) | Summary: This paper considers a multi-round decentralized training method with a limited annotation budget to label data from multiple distributions from various locations.
Claims And Evidence: Yes, the claims have theoretical proofs besides empirical experiments.
Methods And Evaluation Criteria: Yes, the paper shows... | Rebuttal 1:
Rebuttal: Dear reviewer WqSu,
Thank you for your questions. We would like to further clarify the problem and our contributions.
**Problem Definition and Connection to Decentralized Data Collection:** The adaptive data collection problem is indeed related to many topics in active learning (AL), including t... | Summary: This paper proposes a framework for adaptive data collection aimed at robust learning in multi-distribution scenarios under a fixed data collection budget. The proposed algorithm dynamically selects a data source for sampling in each round, updates the model parameters using gradient descent, and repeats the p... | Rebuttal 1:
Rebuttal: Dear reviewer p2BW,
Thank you for your questions. We would like to further clarify the intuitions of the problem and our contributions.
**Necessity of Adaptive Data Collection (Q1):** In our motivating example of vehicle detection in a smart city intersection (*Section 1*), DL models such as SSD... | Summary: This paper proposes a framework for adaptive data collection and model training considering the multiple data distributions with the fixed annotation budget, where the goal is to come up with an optimized model that can perform well on all distributions. Through the integration of the upper-confidence bound (U... | Rebuttal 1:
Rebuttal: Dear reviewer y1y9,
Thank you for your detailed feedback. We have provided additional experimental results in an *[anonymous Github](https://anonymous.4open.science/r/icml2025-adaptive4robust-CB0F)* and further clarified the results and our contributions below.
**Additional Active Learning (AL) ... | Summary: This paper presents a new online framework (UCB-OGD) for data collection and model training in a multi-distributional setting with a constraint on sample labeling. In particular, the purpose UCB-OGD is shown to achieve a sublinear minimax regret with a lower-bound showing algorithmic completion, guaranteeing p... | Rebuttal 1:
Rebuttal: Dear reviewer 7Pps,
Thank you for your feedback.
We have provided additional experimental results in an *[anonymous Github](https://anonymous.4open.science/r/icml2025-adaptive4robust-CB0F)*. There are three active learning curves (model performance *v.s.* number of annotated samples) for three d... | null | null | null | null | null | null |
Identifying Neural Dynamics Using Interventional State Space Models | Accept (poster) | Summary: The paper introduces interventional state space models (iSSM), a novel framework designed to predict neural responses to novel perturbations, addressing the limitations of traditional state space models (SSM) which capture statistical associations without causal interpretation. The authors establish the identi... | Rebuttal 1:
Rebuttal: We sincerely appreciate you and the other reviewers for your time and thoughtful evaluation of our work. We found the feedback to be highly constructive, as well as both fair and encouraging. Below we address your specific questions:
**Nonlinearity limitation:** We completely agree with the revie... | Summary: The paper introduces Interventional State Space Models (iSSM), a class of causal state-space models designed to identify neural circuit dynamics and predict responses to causal manipulations, addressing the limitations of traditional state-space models (SSMs) which lack causal interpretability. By explicitly m... | Rebuttal 1:
Rebuttal: We sincerely appreciate you and the other reviewers for your time and thoughtful evaluation of our work. We found the feedback to be highly constructive, as well as both fair and encouraging. Below we address your specific questions:
**Correct vs. incorrect trials:** Please notice the latents clo... | Summary: The authors propose iSSM, which is a linear dynamical systems model that accounts for causal perturbations to the neural population activity. The authors apply this model to two synthetic datasets inspired by literature on motor cortex dynamics, and apply their model to a variety of real neural population data... | Rebuttal 1:
Rebuttal: We sincerely appreciate you and the other reviewers for your time and thoughtful evaluation of our work. We found the feedback to be highly constructive, as well as both fair and encouraging. Below we address your specific questions:
**Generalizing to other SSMs:** Thank you for bringing this up.... | Summary: This paper provides an extension of the state space model (SSM) to an interventional SSM (iSSM). iSSM is able to causally identify and infer external inputs as an intervention to the neural dynamics, while also inferring the latent and reconstruct the observation accurately. Methods, assumptions, and derivatio... | Rebuttal 1:
Rebuttal: We sincerely appreciate you and the other reviewers for your time and thoughtful evaluation of our work. We found the feedback to be highly constructive, as well as both fair and encouraging. Below we address your specific questions:
**Definition of reconstruction accuracy:** We noticed that the ... | null | null | null | null | null | null |
Efficiently Serving Large Multimodal Models Using EPD Disaggregation | Accept (poster) | Summary: --- score updated from 3 (weak accept) to 4 (accept) after the rebuttal. ---
----
Recently Disaggregated Inference was proposed to use separate nodes for prefill and decoding whilst serving LLMs. This allows to more easily control SLO times like inter token latency or time to first token.
The paper gen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments, recognition of the importance and timeliness of our work, and the positive assessment of our empirical validation. Below we address the main concerns raised:
---
### **Q1: Cost per served request and disaggregation tradeoffs**
> _"I would propo... | Summary: To address the negative impact of the multimodal encoding stage on key Service Level Objectives (SLOs), this paper proposes the Encode-Prefill-Decode (EPD) Disaggregation framework, which allocates the encoding, prefill, and decode stages to independent computing resources. Specifically, this work introduces: ... | Rebuttal 1:
Rebuttal: Thanks you! Glad to hear that the **motivation, design, and experimental results were found to be compelling**.
Below we address the specific questions raised.
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### **Q1: Why does TPOT perform better in the w/o Opt setting compared to w Opt in Table 4?**
Thanks for pointing it out, here is ... | Summary: After rebuttal: Thank the authors for the detailed comments. I'm keeping my recommendation of 3.
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The authors propose a novel Encode-Prefill-Decode (EPD) disaggregation framework for Large Multimodal Model (LMM) inference. The proposed approach decouples encoding and prefill ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback. Below, we address the specific concerns.
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### **Q1: Use of the Term “LMM” and Generalization Beyond Vision-Language Models**
We deliberately use the term **LMMs** instead of “VLMs” to reflect the **general applicability of E... | null | null | null | null | null | null | null | null |
GSM-$\infty$: How Do your LLMs Behave over Infinitely Increasing Reasoning Complexity and Context Length? | Accept (poster) | Summary: This paper introduces a new benchmark for testing the performance of LLMs on long-context reasoning.In particular, the authors start from the abstract computational graphs of the GSK-8K problems and develop the GSM-Infinite benchmark.With the constructed benchmark, the authors observe a consistent sigmoidal de... | Rebuttal 1:
Rebuttal: **Concern about Practicality of GSM-Infinite**
We appreciate the reviewer’s concern about the practicality of GSM-Infinite, as the problem is crucial for understanding why we believe GSM-Infinite is helpful for researchers to use. We want to kindly rebut that first, different from “many syntheti... | Summary: The paper introduces GSM-∞, a synthetic benchmark for evaluating long-context LLMs on grade-school math problems. It generates problems with controllable complexity (via computation graphs) and arbitrary context length (via "spider topology" noise). The paper finds that:
1. Sigmoid performance decay as rea... | Rebuttal 1:
Rebuttal: Additional figures and tables in the link (anonymous):
https://docs.google.com/document/d/1WP3ygB67yNUS-iliSYYjFRY4Ls81yIVcRVxpbTVTZ0o/edit?usp=sharing (referred to as **Sup**)
**1. No ablation on different graph structures.**
Thank you. We agree studying graph structure impact is valuable. We... | Summary: This paper introduces GSM-∞, a synthetic long-context reasoning benchmark generated entirely by an automatic system with fine-grained control over complexity and information density. Specifically, it generates the benchmark by modifying operations like "+ -" in the computational graphs of existing benchmarks, ... | Rebuttal 1:
Rebuttal: **Response to other reasoning relationship question**
We want to express our great appreciation to the reviewer for your positive feedback on GSM-Infinite. The question raised is also highly insightful and greatly appreciated. We want to clarify that the goal of GSM-Infinite is to model all the ... | Summary: The paper introduces a new benchmark designed to evaluate the reasoning capabilities of Large Language Models (LLMs) in long and complex contexts. The benchmark is inspired by the abstraction of GSM-8K problems as computational graphs and introduces a methodology to generate grade-school math problems with in... | Rebuttal 1:
Rebuttal: **Concern GSM-Infinite not practical**
We appreciate the reviewer’s concern about GSM-Infinite’s practicality. While GSM-8K has been a standard for evaluating LLM math reasoning, its difficulty has become saturated (Figure 5(a), Page 4), which is why newer models have moved on. GSM-Infinite buil... | Summary: This paper points out issues in existing long-context reasoning benchmarks and addresses the issues by designing new benchmark called GSM-Infinite.
The main method they use is to construct computation graphs for problems in GSM8K and generate question-answer pairs with user-definable question difficulty measu... | Rebuttal 1:
Rebuttal: **Three missing citations**
Greatly appreciate the comments. We acknowledge that more discussion of previous multi-hop commonsense reasoning benchmarks **will be added in the paper later version**. But these three papers address different problems from ours.
Firstly, HotpotQA [2] and MusiQue [3... | null | null | null | null |
EasyInv: Toward Fast and Better DDIM Inversion | Accept (poster) | Summary: This paper introduces **EasyInv**, an additional mixture operation integrated into the diffusion model-based image editing pipeline, aiming to **enhance inversion accuracy** and **improve computational efficiency**.
## update after rebuttal
1. I acknowledge the approximation of the Kalman Filter and additi... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. The novelty of our work is well recognized by the other two reviewers. However, we received the lowest score from Reviewer md9u. We appreciate your feedback and hope our explanations below address your concerns, leading to a score reconsideration.
**1. Over-Sim... | Summary: The paper introduces EasyInv, a novel approach to DDIM inversion that improves efficiency and reconstruction quality by refining inversion noise approximation. The novelty compared to other inversion methods lies in the addition of a relaxation step, which makes it compatible with other inversion methods. The ... | Rebuttal 1:
Rebuttal: Dear Reviewer UW4y,
We would like to express our sincere gratitude to you. It is truly an honor and a stroke of luck to have a reviewer as dedicated and responsible as you. Your constructive suggestions have been invaluable to us.
We are particularly touched by your **understanding regarding our ... | Summary: This paper introduces EasyInv, a novel DDIM inversion method that significantly enhances inversion efficiency and reconstruction quality by optimizing the utilization of the initial latent state. The work demonstrates thorough experimentation and good practical value, with solid theoretical foundation, making ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s constructive feedback and suggestions for additional experimental validation. Due to space constraints, some of these analyses were deferred to future work; however, several of the suggested experiments have either been completed or are actively underway. We address th... | null | null | null | null | null | null | null | null |
STAR: Learning Diverse Robot Skill Abstractions through Rotation-Augmented Vector Quantization | Accept (spotlight poster) | Summary: This paper proposes STAR, a novel framework for learning diverse robot manipulation skills through skill quantization and causal modeling. STAR consists of two key components: RaRSQ, which enhances residual skill quantization with a rotation-based gradient mechanism to mitigate codebook collapse, and CST, a tr... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our extensive experiments and the practical effectiveness of our approach. We address your questions below:
## Q1：Have the authors considered comparing their method against QueST equipped with an offset prediction to isolate t... | Summary: The paper investigates robot skill abstraction for manipulation tasks and introduces STAR—a framework for learning discrete robot skill representations. STAR comprises two main components: Rotation-Augmented Residual Skill Quantization (RaRSQ), which mitigates codebook collapse in VQ-VAE-based methods using ro... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and positive assessment of our work. Below we address the specific questions and concerns:
## R1: A broader discussion on recent progress in robot learning—especially work on vision-language-action (VLA) transformers like π0—would provide valuable context reg... | Summary: The paper proposes to improve prior latent discrete policies by preventing codebook collapse, improving the codebook utilization and proposes to use an autoregressive poiicy to chain together the various discrete skills. To achieve better codebook utilization (and preventing code collapse), the paper proposes ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer G1Cu for the thorough and positive assessment of our work. We appreciate your recognition of our key contributions in improving codebook utilization through rotation-augmented residual skill quantization and implementing autoregressive decoding for effective skill compo... | null | null | null | null | null | null | null | null |
Hyper: Hyperparameter Robust Efficient Exploration in Reinforcement Learning | Accept (poster) | Summary: This submission proposes a novel method, referred to as Hyper, to address the challenging issue of hyperparameter tuning in curiosity-based exploration methods. It introduces a repositioning and exploration mechanism that controls the horizon of exploitation before conducting exploration. The length of the exp... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and insightful questions. We appreciate your recognition of our work's value and address your questions below.
***Regarding Hyper’s efficiency***
Our claim that "Hyper is efficient" operates on two complementary levels:
1. **Theoretical guarantees**: Hyper... | Summary: This paper proposes a “repositioning_length” based method to alternate between exploration and explosion. The key idea is to choose the bounded geometric distribution with probability p to determine the repositioning_length to make the process more sample efficient.
Claims And Evidence: Yes the motivation is ... | Rebuttal 1:
Rebuttal: We appreciate your feedback, though we must respectfully note that **your summary appears to significantly mischaracterize our paper's contributions and scope**. Your summary focuses narrowly on a single implementation detail (the bounded geometric distribution) without acknowledging our paper's c... | Summary: The paper addresses hyperparameter sensitivity in curiosity-driven RL exploration, proposing **Hyper**, a two-phase algorithm that decouples exploration (curiosity-driven) and exploitation (repositioning-guided). Theoretical guarantees under linear MDP assumptions and empirical validation across navigation/loc... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback, and we appreciate your positive comments. We will address your concerns below.
***Regarding relevant literatures***
Thank you for the valuable suggestion. We will incorporate discussions on the references you suggested in the camera-ready version.
***Regard... | Summary: This paper has proposed a new method, referred to as "hyper-parameter robust exploration (Hyper)", which aims to mitigate the "extensive hyper-parameter tuning" problem in existing curiosity-based exploration methods. The proposed method Hyper is summarized in Algorithm 1. This paper also analyzes Hyper under ... | Rebuttal 1:
Rebuttal: Thank you for recognizing the strengths of our work, particularly our careful experiment design and comprehensive literature review.
Regarding your concerns about theoretical aspects, we want to clarify that the theoretical analysis serves as a convergence guarantee. Hyper's exploration framework... | null | null | null | null | null | null |
Diffusion Models are Secretly Exchangeable: Parallelizing DDPMs via Auto Speculation | Accept (poster) | Summary: This paper proposes and analyzes a parallel sampling scheme for diffusion models. The scheme is simple and natural -- instead of taking a single step from $x_t$ to $x_{t-1}$ during the reverse process in each iteration, multiple steps are taken in parallel from the expected positions $y_s$ given the position $... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and for taking the time to engage with our work. We hope to address your concerns below:
## Comparing ASD with Prior Works
Prior works based on Picard Iterations *can only produce approximate samples* from the DDPM output distribution. In particular, these ap... | Summary: Diffusion models can be expensive to sample from, since sampling involves integrating a certain stochastic process, and is hence autoregressive (first one needs to take one step, then another step conditional on the result of the previous step, and so on). It would be extremely nice if there were some way to p... | Rebuttal 1:
Rebuttal: $\newcommand{\d}{\mathsf{d}}$
$\newcommand{\vx}{\mathbf{x}}$
$\newcommand{\vy}{\mathbf{y}}$
$\newcommand{\vz}{\mathbf{z}}$
Thank you for your thoughtful review and insightful questions which have helped improve our work. We hope to address your concerns below:
## Hidden Exchangeability Beyond O... | Summary: This paper reveals the hidden exchangeability inherent in Denoising Diffusion Probabilistic Models (DDPMs) and proposes Autospeculative Decoding (ASD), a novel algorithm that leverages the model itself to generate multi-step speculations and verifies them in parallel. By eliminating auxiliary draft models, ASD... | Rebuttal 1:
Rebuttal: $\newcommand{\bE}{\mathbb{E}}$
Thank you for your review and for taking the time to engage with our work. We hope to address your concerns below:
## Analysis of Discrete Steps
We highlight that **all our theoretical guarantees for ASD directly consider the discrete-time regime**. To this end, ou... | null | null | null | null | null | null | null | null |
Toward a Unified Theory of Gradient Descent under Generalized Smoothness | Accept (poster) | Summary: ---------
## Update after rebuttal
The authors showed that the issue in the proof can be fixed. I checked the corrected proof and it looks good to me, so I'm raising my score.
-------------
This paper studies the convergence of gradients descent under a generalized assumption on the objective $L$-smoothne... | Rebuttal 1:
Rebuttal: Thank you for the review!
We now start with "Mistake in the proof." Thank you again for spotting a typo in the paper. **We will try to explain that this is a typo rather than a mistake because it is sufficient to fix "max" to "min," and nothing else should be changed.** Let us clarify this part. ... | Summary: This work improves the convergence rates of gradient descent on the $\ell$-generalized smooth problem for both nonconvex and convex settings by using a novel integral-based stepsize. Then it extends the results to stochastic gradient descent algorithm.
Claims And Evidence: The improved convergence rates claim... | Rebuttal 1:
Rebuttal: Thank you!
> Please plot the learning curves which makes this criterion more clear.
We've done this. We will add the following plots to the section with experiments: [figure](https://figicml.tiiny.site/)
> Weakness mainly in presentation: (1) In ICML papers, usually Section 1 is introduction w... | Summary: The paper discusses optimization under the generalized smoothness assumptions and show a choice of step size that improves the known convergence bounds in this setting. The paper also presents convergence rates in scenarios where previous work did not consider (e.g., $\rho\geq 2$).
# Update after rebuttal
In... | Rebuttal 1:
Rebuttal: Thank you!
Let us respond to the weaknesses and questions:
> The fact that the new step size does not always have a closed-form expression could pose challenges for its practical implementation in real-world applications.
We now show how we implement it in the experiments and how it can be done... | Summary: This paper studies the performance of the Gradient Descent (GD) method under a generalized $\ell$-smoothness condition. The authors propose a universal step size applicable across different parameter choices. Using this step size, they improve existing theoretical results and establish new convergence guarante... | Rebuttal 1:
Rebuttal: Thank you for the positive review!
We would like to clarify the weaknesses:
> Can these results be extended to the constrained setting?
This is a good question. We have not yet explored this extension in depth; however, it appears that the constrained setting might be more challenging than in t... | null | null | null | null | null | null |
Independence Tests for Language Models | Accept (spotlight poster) | Summary: This paper introduces a method to assess whether two large language models (LLMs) are independent or if their training procedures exhibit dependencies. The core idea is based on the principle that if two LLM weights are independent, the distribution of differences between arbitrary permutations of their weight... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and address some of their concerns below. We will add the references suggested.
$\textbf{Claims and Evidence Comment 1}$
For the constrained test, we in fact guarantee that the test leads to exact p-values under the null hypothesis when two models are indepe... | Summary: The paper investigates a method to determine whether two models’ weights were trained independently (i.e., from different random initializations) or if one model was derived from the other through fine-tuning, pruning, or partial reuse. This is framed as a hypothesis test for independence between two sets of m... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive feedback!
We find the tests also work on smaller-scale models such as the Phi-3 family. Both $\phi_\text{CSH}$ and $\phi_\text{MATCH}$ return a statistic approximately 1e-308 (aggregated with Fisher’s method) on the fine-tuned model pair microsof... | Summary: This paper introduces a rigorous statistical framework for testing whether two language models were trained independently. Concretely, the authors propose hypothesis tests in both constrained and unconstrained settings. The constrained setting assumes known model architecture and training conditions, allowing ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive feedback!
$\textbf{Weakness 1}$
Thank you for bringing up this concern. Standard machine learning algorithms such as SGD which are the most common algorithms for training language models follow the equivariance condition, as the gradients are p... | Summary: The paper addresses the large model independence test: given the weights of two language models, can we determine if they were trained independently or if one model’s weights are derived from the other? Leveraging permutation invariance and equivariance in MLP neurons, it provides exact p-values. Extensive eva... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and feedback! We will add the references mentioned.
$\textbf{Experimental Designs Or Analyses Comment 1}$
We report experiments on Llama 70B, the hybrid StripedHyena and Mistral model, and (distilled) GPT 2 models in Table 5, 9, and 15 and find our statist... | Summary: This paper proposes a statistical test for determining whether the initializations of two language models (really, "deep networks containing GLU MLPs", or even really slightly weaker than that) are independent or not, when treating the algorithms themselves (and any data they use, etc) as fixed. Exactly valid ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and positive feedback! We will add the Hemerik and Goeman reference mentioned, thank you.
About some of the weaknesses discussed:
From our empirical results, the power of our constrained and unconstrained test is as low as 2.2e-308 — in the cases of rejecti... | null | null | null | null |
Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models | Accept (poster) | Summary: This work proposes a large-scale federated full parameter tuning framework for LLM, Ferret, which mainly exploits the first-order method of shared randomness. The proposed method mainly consists of three steps: using first-order methods for efficient local updates, projecting these updates into a low-dimension... | Rebuttal 1:
Rebuttal: We thank Reviewer CfcA for recognizing the comprehensive theoretical analysis, solid related work, and clarity of our paper. We would like to address your concerns below:
> W1: Performance Comparison with LoRA methods.
- **Clarification on Scope**: Our work focuses on **full-parameter fine-tunin... | Summary: In this work, the authors proposed Ferret, a federated learning method for efficient full-parameter fine-tuning of LLMs, combining first-order optimization with random projection-based dimensionality reduction. It uses shared randomness to reconstruct local updates at the server, significantly reducing communi... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough evaluation and constructive feedback. We address the points raised below:
> Claims And Evidence
- We appreciate the reviewer's feedback on the comparison to FetchSGD. While both methods use dimensionality reduction, Ferret's technical approach and goal are ... | Summary: This paper introduces Ferret, a method for full-parameter tuning of large language models (LLMs) in federated learning. It primarily addresses the challenge of communication overhead by combining the strengths of first-order optimization (efficient computation and fast convergence) and zeroth-order optimizatio... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer oeGQ for the positive evaluation, particularly recognizing our theoretical analyses, experimental results, and the novelty of our approach. We address the reviewer's concerns below:
> W1: Reconstruction Error
We clarify that the slight performance difference compared ... | null | null | null | null | null | null | null | null |
Improving the Statistical Efficiency of Cross-Conformal Prediction | Accept (poster) | Summary: This paper proposes several new variants of (modified) cross-conformal prediction--called e/u/eu-modified cross conformal prediction--that theoretically and empirically attain more efficient (i.e., smaller and thus more informative) prediction sets/intervals than the original (modified) cross-conformal predic... | Rebuttal 1:
Rebuttal: Thank you very much for your positive and constructive comments on our paper. Please find below a detailed response to your questions.
### Experimental Designs:
- To improve clarity, we will add the target coverage level $1-\alpha=0.9$ in the captions.
- As index of variability, in Table 1, we re... | Summary: The authors move from the interesting, yet relatively limited literature on methods that work on improving the dramatic data inefficiency of split conformal prediction.
Their proposal exploits novel results about p-value combination to introduce a variant of the well known cross-conformal prediction methods th... | Rebuttal 1:
Rebuttal: We appreciate your feedback. Below is our response to your comments
# Experimental Designs
We now additionally analyze a real dataset on electricity consumption, where accurate uncertainty quantification is crucial as the supplier’s revenue depends on customer energy use. The dataset contains 35,4... | Summary: The paper proposes new variants of cross-conformal prediction to obtain smaller prediction sets while guaranteeing the same worst-case miscoverage rate. They use recent results on the combination of dependent and exchangeable p-values to obtain their results. Empirical evaluation on simulated data and the news... | Rebuttal 1:
Rebuttal: Thank you very much for your positive feedback on our paper. We truly appreciate your comments.
Thank you for noticing the typo, we corrected it. | Summary: The authors propose new variants of cross-conformal prediction that leverages recent results on combining p-values through exchangeability and randomization. They theoretically demonstrate that their methods can reduce the size of the prediction set while maintaining a marginal coverage of at least $1 - 2\alph... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper. Please find below a detailed response to your concerns.
### Experimental Designs Or Analyses:
Thank you for the suggestions. Below, we present an example where the experimental setting is identical to the last experiment in Appendix E, but with $K$ set to... | null | null | null | null | null | null |
Towards Cost-Effective Reward Guided Text Generation | Accept (poster) | Summary: In earlier works on reward guided text generation (RGTG), the reward model is usually operationalized with a regression head on top of LM. However, this comes at the cost of having to evaluate all next possible tokens V times. This paper proposes a simple change that turns this into a V-channel head, just like... | Rebuttal 1:
Rebuttal: We want to thank you for reviewing our paper and your strong endorsement of our work. | Summary: This paper proposes an improved reward model for reward-guided text generation (RGTG), an alternative to offline RLHF for aligning language models with human preferences. Traditional RGTG incurs high inference costs as reward models score tokens individually and are optimized for full sequences, leading to sub... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and numbering your comments.
# W1:Performance Advantage on larger models
We would like to point out the result on the 7 billion model (Table 5 Appendix) is on a different dataset, Ultra Feedback, and the absolute reward values on different datase... | Summary: The paper proposes a new method to do reward-guided text generation (RGTG) that prefers the optimal expansion of a sequence and needs only one call to score all candidate tokens simultaneously. The experiments show that the proposed reward model leads to faster inference than other RGTG methods and performs on... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and questions. We hope that our response will satisfy your concerns.
# 1. FaRMA Efficiency vs DPO
DPO is more expensive to train because it loads and makes calls to an additional reference model, $\pi_{ref}$, along with the model which is trained, $\pi_\theta$... | null | null | null | null | null | null | null | null |
Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures | Accept (spotlight poster) | Summary: This paper presents a novel geometric framework for learning unknown dynamics under environmental constraints when constraint information is only locally available and uncertain. The authors introduce a fiber bundle structure over the state space that unifies measurements, constraints, and dynamics learning. T... | Rebuttal 1:
Rebuttal: Thank you for your feedback.
## Experimental Details & Baselines
Our Genesis physics engine uses task-specific configurations: Semi-implicit Euler (5e-4s timestep) with neo-Hookean material (μ=2kPa, λ=10kPa) for soft worm; RK4 (1e-2s timestep) with friction coefficients (0.15 static, 0.09 dynami... | Summary: This paper considers the problem of learning unknown dynamics models in the presence of model constraints. The paper points out that classical treatments of this problem ignore the system's inherent geometry while taking measurements into account and, in doing so, ignore important information that could be use... | Rebuttal 1:
Rebuttal: We appreciate your evaluation and suggestions. Your positive assessment is encouraging, and we accept the improvements you suggested. Below are the enhancements we plan to implement:
## Correction of Theoretical Statements
We will add the dependency on $\alpha$ in equation (9), correcting it to ... | Summary: This study proposes a geometric approach to learning dynamics with safety guarantees, leveraging the bundle structure to account for uncertainties. After presenting the geometric approach for controlled dynamical systems, the study introduces measurement-adapted control barrier functions, which enable the deve... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback and have prepared appropriate revisions.
## On Theorem 3.1 Formulation
We appreciate that you correctly noted that the theorem should explicitly specify the control law. We will revise Theorem 3.1 to clearly indicate that there exists a control strategy ... | null | null | null | null | null | null | null | null |
Pareto-Optimal Fronts for Benchmarking Symbolic Regression Algorithms | Accept (poster) | Summary: The authors aim to generate a set of "absolute pareto optimal" model results for 34 of the datasets in SRBench, as a way of having an upper bound on the performance of symbolic models on those datasets up to a given equation length. They exhaustively search for equations for those datasets up to a given length... | Rebuttal 1:
Rebuttal: > General
Please refer to (G.1).
> Claims
(D.1) We thank the reviewer for the suggestions for “Convention #1”. We will improve the clarity of our discussion by explicitly stating that our comments refer only to Pareto fronts/optimality and not statistical tests in (Demšar, 2006).
The reviewer n... | Summary: One common way to evaluate symbolic regression (SR) algorithms is to judge whether one method Pareto-dominates other SR algorithms. That means it has better performance for a given expression length. This paper proposes to evaluate SR methods against absolute Pareto-optimal solutions instead. It finds an absol... | Rebuttal 1:
Rebuttal: > General
(G.1) We thank all 4 reviewers for their comprehensive reviews, identifying strengths of the work (e.g., “the authors have given concrete targets for future SR algorithm development”, “addresses a critical gap in SR benchmarking, where prior evaluations lacked universal reference point”... | Summary: This paper proposes an absolute evaluation criterion for symbolic regression, namely the Absolute Pareto Optimality (APO). At the same time, the effects of eight different optimization algorithms are analyzed. The establishment of this criterion is of great significance, as it provides a fair and reliable benc... | Rebuttal 1:
Rebuttal: > General
Please refer to point (G.1) in the response to Reviewer Gtam.
> Claims, Methods and Evaluation, Experimental Designs, Supplementary, Broader Literature, References
(B.1) We thank the reviewer for the validation.
> Other Strengths And Weaknesses
(B.2) We thank the reviewer for the ref... | Summary: This paper proposes the absolute Pareto optimal (APO) front as a new benchmarking methodology for evaluating symbolic regression (SR) algorithms. Conventional SR evaluation is based on relative Pareto dominance with respect to other algorithms, but this does not provide any measure of efficiency or achievable ... | Rebuttal 1:
Rebuttal: > General
Please refer to point (G.1) in the response to Reviewer Gtam.
> Claims And Evidence
(A.1) We thank the reviewers for advice on our discussion on the subtleties with the term “absolute” in the limitations section that includes our mitigation strategies. To further improve the clarity, ... | null | null | null | null | null | null |
Tensorized Multi-View Multi-Label Classification via Laplace Tensor Rank | Accept (poster) | Summary: In this paper, the authors propose a novel approach that introduces a low-rank tensor classifier combined with the innovative Laplace Tensor Rank (LTR), which jointly captures high-order feature correlations and label dependencies. Extensive experiments across six benchmark datasets demonstrate TMvML’s superio... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our paper. We appreciate the time and effort you have put into reviewing our work. In this rebuttal, we respond to the concerns raised in the reviews.
**W1:** In this figure, we tested the ability of multiple low-rank tensor norms (including TNN, ETR, LTSpN and LTR) ... | Summary: This paper proposes a method named TMvML for multi-view multi-label learning (MVML). The approach includes a low-rank tensor classifier to capture both consistent correlations across views and modeling complex multi-label relationships. Additionally, a new Laplace Tensor Rank (LTR) is introduced to capture hig... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our paper. We appreciate the time and effort you have put into reviewing our work. In this rebuttal, we respond to the concerns raised in the reviews.
**W1**: We agree that comparing the proposed Laplace Tensor Rank (LTR) with the widely used Tensor Nuclear Norm (TNN... | Summary: This paper presented a method for Multi-View Multi-Label Learning (TMvML) which utilizes tensorized MVML classifier to achieve the high-order feature correlations extraction and multi-label semantic relationships characterization simultaneously. Moreover, a new Laplace Tensor Rank is designed to characterize a... | Rebuttal 1:
Rebuttal: Thank you for the feedback on our paper. We appreciate the time and effort you have put into reviewing our work. In this rebuttal, we respond to the concerns raised in the reviews.
**W1(C1):** Existing tensor-based methods have primarily been applied to multi-view clustering tasks for mining high... | Summary: This paper proposes a Tensorized Multi-View Multi-Label Classification (TMvML) method to address the limitations of existing approaches that independently model cross-view consistent correlations and multi-label semantic relationships in MVML learning. The method reconstructs multi-view multi-label mapping mat... | Rebuttal 1:
Rebuttal: **W1**: The convergence of TMvML is guaranteed through the validation presented in Theorem 1, with comprehensive and rigorous proof below.
**Theorem 1**: Let $\\{\mathcal{P}_k = ({\bf Z}_k^{v}, {\bf E}_k^{v}, {\bf A}_k^{v}, {\bf W}_k^{v}, {\bf B}_k^{v}, \mathcal{C}\_k, \mathcal{G}\_k)\\}\_{k=0}^{... | null | null | null | null | null | null |
PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning | Accept (poster) | Summary: This paper introduces PIGDreamer, a novel model-based reinforcement learning approach designed to enhance safety and performance in partially observable environments by leveraging privileged information during training. The authors propose Asymmetric Constrained Partially Observable Markov Decision Processes (... | Rebuttal 1:
Rebuttal: We thank Reviewer Rrit for your thoughtful comments and questions. Your insights significantly contribute to the further enhancement of our paper.
**Q1. A more comprehensive discussion of related work.**
Thank you for your suggestion. In the next version of our manuscript, we will discuss recent... | Summary: The work focuses on the development of a novel safe model-based reinforcement learning method. The researchers utilize the concept of privileged information and introduce the so-called asymmetric constrained partially observable Markov decision process (ACPOMDP) task, which requires access to the actual state ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the valuable suggestions provided by Reviewer OU54. In response, we have conducted additional experiments and clarifications, with the hope that these efforts will address your concerns.
**Q1. Clarification Regarding Model Parameters and Deployment.**
The naive and privil... | Summary: Disclosure: I am an emergency reviewer, and also quite familiar with the paradigm of RL with privileged information.
The authors propose to exploit privileged information for policy learning in the context of safe RL. They use a world model approach like Dreamer, where an actor-critic is trained solely in a l... | Rebuttal 1:
Rebuttal: We would like to express our gratitude to Reviewer byT1 for your insightful comments.
**Q1. The mismatch arises from the Twisted Imagination (TI) and the ablation of TI with the Nested Latent Imagination (NLI).**
Thank you for your question. Indeed, the trajectories generated from TI exhibit var... | null | null | null | null | null | null | null | null |
Enhancing Logits Distillation with Plug&Play Kendall's $\tau$ Ranking Loss | Accept (poster) | Summary: This paper presents a method to enhance logits distillation by incorporating a ranking loss based on Kendall’s τ coefficient. The main finding is that the conventional knowledge distillation approach, which relies heavily on KL divergence, often neglects smaller logit channels that contain valuable information... | Rebuttal 1:
Rebuttal: We sincerely thank you for the in-depth feedback! We highly value each of your comments, and all your concerns are addressed point by point:
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**Q1: Claims And Evidence**
1. **Small gradient**: We would like to clarify that while small gradient information does not necessarily imply ineffectiv... | Summary: This paper introduces a plug-and-play ranking loss based on Kendall’s τ to mitigate two drawbacks of KL-based logit distillation: the neglect of low-probability channels and getting stuck in suboptimal points. By aligning channel-wise rankings between teacher and student, it leverages inter-class relationships... | Rebuttal 1:
Rebuttal: We sincerely thank you for the in-depth feedback! We highly value each of your comments, and all your concerns are addressed point by point:
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**Q1. Font Size in Figure 1:**
- Thank you very much for your feedback regarding the formatting of our paper. We will will use appropriate font sizes ... | Summary: This paper points out two drawbacks of KL divergence in knowledge distillation: (1) it is often prone to suboptimal points; (2) it overlooks low-probability channels. The authors use Kendall’s $\tau$ coefficient to mitigate these issues and better model inter-class relationships. Experiments on CIFAR-100, Imag... | Rebuttal 1:
Rebuttal: We sincerely thank you for the in-depth feedback! We highly value each of your comments, and all your concerns are addressed point by point:
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**Q1. The scale of KL is influenced by both the teacher’s probability and the logarithmic term. Could the authors clarify the specific role of the loga... | Summary: This paper proposes an auxiliary ranking loss based on Kendall’s tau coefficient to improve knowledge distillation by addressing the limitations of KL divergence. Traditional KL-based distillation struggles with optimization challenges and tends to overlook low-probability channels, leading to suboptimal perfo... | Rebuttal 1:
Rebuttal: We sincerely thank you for the in-depth feedback! We highly value each of your comments, and all your concerns are addressed point by point:
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**Q1. Discuss More techniques of detection improvement:**
- We sincerely appreciate your suggestion. Exploring the application of the proposed method ... | null | null | null | null | null | null |
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency | Accept (spotlight poster) | Summary: This work investigates uplift modeling for incentive allocation in digital marketplaces, addressing two critical challenges: OOD generalization and selection bias mitigation through spurious correlation elimination. The proposed IDUM framework introduces: 1) A causal invariance learning mechanism identifying d... | Rebuttal 1:
Rebuttal: Dear Reviewer UCNa,
Thank you for taking the time to review our work, we sincerely appreciate your insightful comments, which have helped improve our paper. Below, we provide point-by-point responses to each concern raised.
1. **Mathematical Formulations**: We appreciate the valuable feedback an... | Summary: This study addresses uplift modeling for incentive allocation in online markets, aiming to resolve out-of-distribution generalization challenges and selection bias by eliminating spurious correlations. The authors propose the Invariant Deep Uplift Modeling (IDUM) framework, which innovatively integrates a cros... | Rebuttal 1:
Rebuttal: **Dear Reviewer 4KLk**,
Thank you for taking the time to review our work, we sincerely appreciate your insightful comments, which have helped improve our paper. Below, we provide point-by-point responses to each concern raised.
**Cons**
1. **Open-source code**: We have included our code in the ... | Summary: The paper proposes Invariant Deep Uplift Modeling (IDUM) for incentive assignment in online marketing (e.g., coupons or discounts). The model identifies features that are both necessary and sufficient under distribution shifts (e.g., changes in user demographics, time, or geography). It builds on the Probabili... | Rebuttal 1:
Rebuttal: **Dear Reviewer UDxr**,
Thank you for taking the time to review our work, we sincerely appreciate your insightful comments, which have helped improve our paper. Below, we provide point-by-point responses to each concern raised.
**Weaknesses:**
1. **Additional experiments**:
Due to the inhere... | Summary: This paper introduces the IDUM method for predicting uplift in an out-of-distribution setting. It utilizes a Gumbel Softmax-based feature selection mechanism to identify a relevant subset of features, followed by invariant property learning. Additionally, the balancing discrepancy component mitigates selection... | Rebuttal 1:
Rebuttal: **Dear Reviewer xxdc**,
Thank you for taking the time to review our work, we sincerely appreciate your insightful comments, which have helped improve our paper. Below, we provide point-by-point responses to each concern raised.
**Methods And Evaluation Criteria:**
We will follow your advice to ... | null | null | null | null | null | null |
An Improved Clique-Picking Algorithm for Counting Markov Equivalent DAGs via Super Cliques Transfer | Accept (oral) | Summary: The paper proposed a more efficient way to improve the existing work by Wienobst et al on counting the number of DAGs in an MEC. The authors argue that the previous approach suffers in the case where there are multiple maximal cliques in a MEC. The proposed improvement is to make use of structure overlaps betw... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback. We address the comments in details below.
>**Weaknesses:***"...the paper can be improved by providing a working example in the appendix..."*
Thanks for the suggestion. We plan to include a table summarizing all key notations and concept... | Summary: This paper proposes an improvement to the clique-picking algorithm introduced by Wienöbst et al. (2023) for counting Markov Equivalent Directed Acyclic Graphs (DAGs). The authors introduce super cliques and super residuals to reduce computational complexity when identifying undirected connected components (UCC... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and remarks. We will address your questions and suggestions below.
> **Other Comments Or Suggestions:**
*"More discussion on the quantitive comparison between the computational complexity of proposed algorithm with the existing work will be appreciated."*
Than... | Summary: This submission presents an improvement of the recent polynomial time algorithm for counting moral acyclic orientations of chordal graphs, a problem which lies at the core of counting Markov equivalent DAGs. The main idea of that algorithm compared to older iterative root-picking algorithms lay in picking root... | Rebuttal 1:
Rebuttal: We greatly thank you for the positive feedback and valuable recognition of our work's core ideas and contributions. | Summary: Paper addresses the computational complexity of finding the size of the so-called Markov equivalence class (MEC) encoding conditional dependencies. Conditional dependence properties are captured by d-separation property of the DAGs and the method counts DAGs in the MEC class (representing the same/equivalent c... | Rebuttal 1:
Rebuttal: We sincerely appreciate your feedback. We are excited that you have found our approach useful and novel. Please find below our response to your concerns.
> **Claims And Evidence:** *"...further visual helpers may improve appeal of the paper to wider community..."*
Thank you for the helpful sug... | null | null | null | null | null | null |
Fusing Reward and Dueling Feedback in Stochastic Bandits | Accept (poster) | Summary: This paper investigates the fusion of numerical and preference feedback in stochastic bandits, where both feedback types are gathered in each timestep. The authors derive a regret lower bound, demonstrating that an efficient algorithm may incur only the smaller among the reward and dueling-based regret for eac... | Rebuttal 1:
Rebuttal: > *W1. [...] other relation between the preference probability and expected reward [...] like the Bradley-Terry model*
**A1:** We thank the reviewer for raising this intriguing question. A relation between the preference probability and expected reward, like the Bradley-Terry model $\nu_{1,2} = \... | Summary: This paper looks into the problem of stochastic bandits with fusing reward and dueling feedback where the regret is defined as linear combination of the normal regret and averaged dueling regret.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: - I checked the lower bound ... | Rebuttal 1:
Rebuttal: > *Q1: The regret bound for No Fusion algorithm seems to be different in the Table 1 and line 224 (right)*
**A1:** Indeed, they are different. The regret bound in Table 1 combines two separate (optimal) regret bounds for the reward and dueling bandits, respectively. Therefore, the deminonator is ... | Summary: The paper investigates a K-armed bandit problem in which, in addition to the standard observations of arm rewards (modeled by a Bernoulli distribution), the learner has the option to select two additional arms at each time step. The feedback received includes comparisons between the selected arms, which are al... | Rebuttal 1:
Rebuttal: > *Q1: the practical value of the paper is not clear. [...] simplistic and niche*
**A1:** **Practical value** While the authors agree that the bandits setting (a framework in favor of theoretical analysis) of this paper is indeed simplified, we believe that the proposed algorithms and theoretical... | Summary: This manuscript considers a new bandit problem where the learner, at each step, simultaneously chooses any of the K arms and any couple of the K arms and observe an absolute reward as well as a dueling reward. The incurred regret is a convex combination (with parameter $\alpha$) of the absolute regret and the... | Rebuttal 1:
Rebuttal: > *W1): In my view, the regret bounds are of high asymptotic nature. [...] where T\geq \exp [ c K], which is quite disappointing.*
**A1:** We respectfully disagree with the reviewer's characterization of our results as "disappointing," particularly regarding the fact that our upper bound matches... | null | null | null | null | null | null |
CogMath: Assessing LLMs' Authentic Mathematical Ability from a Human Cognitive Perspective | Accept (poster) | Summary: The paper introduces CogMath, an evaluation framework for assessing LLMs' capabilities from a human cognitive perspective. It breaks down mathematical reasoning into three stages: problem comprehension, problem solving, and solution summarization. Each stage is further evaluated through nine detailed dimension... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of the psychological foundation of our evaluation framework, the extensive and significant experiments, the valuable insights provided by our work, and the well-organization of our paper.
$\bf{Q1}$: If there are no numerical values in the problem (such as ... | Summary: This paper proposes CogMath to assess LLMs’ abilities at specific cognitive stages. Based on psychological research, they decompose the mathematical reasoning process into three stages: problem understanding, problem solving, and solution summarization. Then, in each stage, they further specify several detaile... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our motivation and solid psychological foundation, the credibility of our experimental results, the significant contributions of our framework, and the good writing of our paper.
$\bf{Q1}$: The decomposition of the reasoning process into three stages co... | Summary: The paper proposes CogMath, a novel evaluation framework for assessing the mathematical reasoning abilities of LLMs from a human cognitive perspective. Traditional benchmarks primarily focus on answer accuracy, often overestimating LLMs’ true mathematical competence. Instead, CogMath structures evaluation into... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our framework's novelty, the validity of our conclusions, and the insights of our work.
$\bf{Q1}$: Validity of Dimension 6.
$\bf{A1}$: Thanks for your insightful comments. First, the strategy of changing numbers in problems has been widely adopted in ... | Summary: This paper introduces CogMath to assess the authentic mathematical reasoning abilities of LLMs through the lens of human cognition. Specifically, the paper models human mathematical reasoning with three stages and nine dimensions, such as sentence paraphrasing, numerical transformation, and backward reasoning.... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our framework's soundness, evaluation significance, and great contributions.
$\bf{Q1}$: Adaptability to more problem types.
$\bf{A1}$: Thanks for your insightful question. Our CogMath is easily adaptable because: (1) it is based on the decomposition of... | Summary: This paper aims to explore and evaluate the mathematical ability of LLMs. The authors propose a novel evaluation framework (CogMath) based on the human psychological design. The workflow examines the LLM’s performance across three stages, including problem comprehension, problem-solving, and solution summariza... | Rebuttal 1:
Rebuttal: We sincerely appreciate your affirmation of our framework's novelty, experimental validity, and evaluation significance.
$\bf{Q1}$: Could include more deepseek version.
$\bf{A1}$: Thanks for your valuable suggestion. We will incorporate more references to DeepSeek-related papers. Besides, we con... | null | null | null | null |
An Online Learning Approach to Prompt-based Selection of Generative Models and LLMs | Accept (poster) | Summary: - This paper frames the task of optimally routing prompt to a data generation model as a contextual bandit problem
- In doing so, the authors design a contextual bandit algorithm called PAK-UCB and prove uapper bounds on its expected regret
- The overcome the computational overhead of PAK-UCB, the authors p... | Rebuttal 1:
Rebuttal: We thank Reviewer 5QGu for the thoughtful feedback on our work. Below is our answer to the reviewer's comments and questions.
**1. Regret and complexity of PAK-UCB and RFF-UCB**
First, we would like to clarify that the numerical results in Section 6 are reported for PAK-UCB (Alg.2) and RFF-UCB ... | Summary: Generative models are increasingly being used in numerous applications. Evaluation scores are typically used when selecting a sample generation from multiple models. The drawback of evaluation scores is that different models perform better under different text prompts. The paper proposes a method to address th... | Rebuttal 1:
Rebuttal: We thank Reviewer 3Dpj for the thoughtful feedback on our work. Below is our answer to the reviewer's comments and questions.
**1. Details of the datasets**
We note that the details of experiment settings are discussed in Appendix D. The following is a summary of the details asked by Reviewer 3... | Summary: This study focuses on the task of selecting the generative model that achieves the highest reward for a given input prompt. The authors formulate this task as a contextual bandit (CB) problem, treating it as an online learning problem where past records are used to update the predictive model dynamically. They... | Rebuttal 1:
Rebuttal: We thank Reviewer MgCP for the thoughtful feedback on our work. Below is our answer to the reviewer's comments and questions.
**1. Arm-specific reward model**
We thank the reviewer for pointing out the bandit algorithm in [1] that utilizes arm-specific prediction functions. To the best of our kn... | null | null | null | null | null | null | null | null |
Beyond Topological Self-Explainable GNNs: A Formal Explainability Perspective | Accept (poster) | Summary: In this paper, the authors argue that existing SE-GNNs generate Trivial Explanations and advocate for designing GNN explanation methods capable of producing Prime Implicant and faithful explanations. After conducting theoretical analysis, they propose DC-GNNs, a dual-channel model that incorporates a non-topol... | Rebuttal 1:
Rebuttal: Thank you for the feedback. See our comments below.
**Summary clarification:** We are *not* advocating for extracting PI explanations from SE-GNNs. As per the Abstract and Sec 6 (line 244), PIs can be intractable to compute and as large as the input. Our analysis is not meant as criticism. In fac... | Summary: This paper investigates the properties and limitations of explanations generated by Self-Explainable Graph Neural Networks (SE-GNNs). The authors formalize SE-GNN explanations as Trivial Explanations (TEs) and compare them to Prime Implicant (PI) and faithful explanations. They find that TEs, while effective f... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We are glad they found our analysis of interest to the community. Below, we address their remarks.
**Not all SE-GNNs are restricted to TEs:** We highlighted multiple times that our focus is on SE-GNNs with the losses of Table 1 (Line 57 in Preliminaries, ... | Summary: This paper discusses several definitions of Graph explanations and there internal connections. They provide analytical results on the connections between TEs, PIs, sufficient and necessary subgraphs explanations. Because they show that TEs are always less informative than other explanations & directly learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We are glad they enjoyed reading it and found it well motivated. Below, we address their remarks.
**Computational resources for running DC-GNNs:** We would like to clarify a slight misunderstanding regarding our discussion of computational costs. Our argu... | Summary: This paper investigates Self-Explainable Graph Neural Networks (SE-GNNs) and their limitations in explanation quality. It formalizes the Trivial Explanations (TEs) generated by SE-GNNs and compares them with Prime Implicant (PI) and faithful explanations. The analysis shows that TEs align with PI explanations ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work. We are glad that you found it well supported by evidence and theoretically sound. We applied the following changes.
**W1: Clarify TEs are a contribution** We revised the Introduction line 16 as follows:
> Focusing on graph classification, we int... | null | null | null | null | null | null |
Primitive Vision: Improving Diagram Understanding in MLLMs | Accept (poster) | Summary: The paper focus on the problem of math diagram understanding --- multimodal math problems with visual inputs. The paper has two main contributions, (1) GeoGLIP, a vision encoder that is more geometrically-grounded; (2) PRIMITIVE, a VLM trained with GeoGLIP + a feature selection module, which demonstrates stron... | Rebuttal 1:
Rebuttal: # We thank the reviewer for insightful questions that help refine our work further.
## 1. Claim 1—that GeoGLIP outperforms CLIP—is unconvincing due to missing baselines using the same VLM and data with original vision encoders, leaving it unclear whether gains come from the backbone or training da... | Summary: This paper addresses a significant challenge in multi-modal large language models (MLLMs), specifically focusing on their limited ability to accurately interpret geometric primitives (e.g., points, lines, boundaries, and junctions) in mathematical diagrams. The authors conduct a comprehensive analysis revealin... | Rebuttal 1:
Rebuttal: # We thank the reviewer for insightful questions that help refine our work further.
## 1. The impact of visual encoders—e.g., comparing Qwen2.5-VL and PRIMITIVE-Qwen2.5-7B with fixed LLM weights.
To address the concern about visual encoders' impact on reasoning, we compare variants in controlled... | Summary: This work proposes a novel approach named PRIMITIVE, aiming to address the deficiencies of current mathematical multimodal large language models (MLLMs) in geometric perception, thereby enhancing their capabilities in visual mathematical reasoning. Experiments conducted on three benchmarks demonstrate the effe... | Rebuttal 1:
Rebuttal: # We thank the reviewer for insightful questions that help refine our work further.
## 1. The authors use existing models to extract junctions and boundaries as ground truth—could this introduce noise given the variation in geometric shapes and domains?
For junction detection, we discuss noisy ... | Summary: This paper proposes PRIMITIVE, a multi-modal large language model for mathematical problem-solving. The contribution of PRIMITIVE is two-folds: a mathematical vision encoder, GeoGLIP, and an MLP-based feature router. The GeoGLIP is pre-trained using synthetic data with box-level and pixel-level loss within the... | Rebuttal 1:
Rebuttal: # We thank the reviewer for insightful questions that help refine our work further.
## 1. The formula (1) in Section 3.3 should contain more comprehensive illustration of newly defined labels.
Thank you. Duty noted. Eq. (1) describes the soft router process. The scalar routing weight for each lev... | null | null | null | null | null | null |
HYGMA: Hypergraph Coordination Networks with Dynamic Grouping for Multi-Agent Reinforcement Learning | Accept (poster) | Summary: The paper proposes a new method to learn higher-order coordination patterns between agents, based on a spectral clustering algorithm and a hyper-graph convolutional network. Agents are coherently grouped together, with such groups changing only when a certain threshold is hit, with the HGCN then combining thei... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We sincerely appreciate your thoughtful questions that will improve our manuscript.
Q1: CTDE Compliance and Execution Requirements
Our method strictly adheres to CTDE without requiring centralized execution:
1.During execution, agents' decisions depend onl... | Summary: The paper presents a novel framework that combines dynamic spectral clustering with hypergraph neural networks to address the multi-agent coordination problem in Multi-Agent Reinforcement Learning. The framework performs spectral clustering on agents’ state histories , dynamically constructing and updating the... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback and constructive comments. Your insights have helped us identify areas for improvement in our manuscript.
Weaknesses 1: Regarding Figure 1
We sincerely appreciate your thoughtful feedback and constructive comments, which have helped us identify ar... | Summary: This paper proposes a multi-agent reinforcement learning framework based on dynamic spectral clustering and hypergraph coordination network, aiming to address the challenges of dynamic relationship modeling and efficient information exchange in complex collaborative tasks.
Claims And Evidence: The core propos... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and insightful questions. Your feedback has helped us identify important areas for clarification and improvement.
Q1: Computational Overhead of Spectral Clustering
We appreciate your question regarding computational scalability. Our implementation add... | Summary: This work considers the problem of coordination in multi-agent systems. It proposes to construct hypergraphs (i.e., graphs with n-ary rather than binary relations) based on agent histories and using a graph convolution technique over this structure. This is motivated by the fact that agents need to form groups... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful feedback, which has helped us improve our manuscript significantly.
M1: Similarity Matrix Construction and State/Observation Formats
In Section 3.2, we presented our dynamic grouping framework using the normalized cut problem. The similarity matrix $W$ is ... | null | null | null | null | null | null |
Big Cooperative Learning to Conquer Local Optima | Reject | Summary: This paper introduces Big Cooperative Learning (BCL), a strategy to circumvent local optima by exploiting multiple “views” of the same data distribution. Instead of using one global objective, BCL sets up many subtasks (e.g., marginal or conditional matching, or transformations of the features). All tasks shar... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thoughtful review and recognition of the contributions of our work. Below, we systematically address each of the raised concerns with additional evidence where appropriate. We welcome further feedback.
**Q1: Statistical significance tests**
In the FKL experiments, Fi... | Summary: This paper introduces "big cooperative learning" (BCL), a learning approach to address local optima challenges in conventional machine learning paradigms. The core concept involves diversely exploiting available information (data samples or energy landscapes) to design multiple cooperative training tasks with ... | Rebuttal 1:
Rebuttal: We appreciate your comprehensive comments with insightful future directions. Below we address your main concerns within the 5000-character limit, with some details consolidated in responses to other reviewers. We welcome further discussion.
**Q1: BCL is the missing core element … is overstated. W... | Summary: This paper focuses on the generative model and discusses several learning objectives, regardless of the model architecture and data.
The authors generalize the conventional learning objective and conditional learning objective to propose the Big Learning, aiming to eliminate the local minima problem.
The autho... | Rebuttal 1:
Rebuttal: We appreciate your comments. As there are misunderstandings, we invite you to read our responses along with other reviewers’ comments, i.e., the comprehensive and insightful comments of Reviewer w4Bu and the concise and objective assessment of Reviewer 4KZD. We welcome further discussion and thank... | null | null | null | null | null | null | null | null |
MMInference: Accelerating Pre-filling for Long-Context Visual Language Models via Modality-Aware Permutation Sparse Attention | Accept (poster) | Summary: The paper addresses the computational bottleneck in long-context Vision Language Models (VLMs) during the pre-filling stage. The authors observe that attention in VLMs exhibits unique sparse patterns, particularly a "Grid" pattern in video inputs due to spatiotemporal locality. They also identify distinct moda... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's recognition and thoughtful, constructive feedback. Below, we address each of the comments and concerns in detail.
1. ***"How stable are the identified patterns across different models and datasets"***
Thank you for the suggestion. We have tested these pattern... | Summary: This proposes a modality-aware permutation sparse attention method that accelerates long-context VLMs, called MAPSparse. It features permutation-based grid sparse attention, Qboundary/2D-boundary patterns for mixed-modality boundaries, and a Modality-Aware Sparse Attention Search Algorithm. Experiments prove... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s thoughtful and constructive feedback. We respond to each of the comments and concerns below.
1. ***"it's better to report average latency and memory consumption in Table 1"***
Thank you for the suggestion. We have updated Table 1 to include a dedicated column... | Summary: MAPSparse provides an innovative and effective solution for accelerating the pre-filling stage of long-context VLMs.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Theoretical Claims: N/A
Experimental Designs Or Analyses: Yes.
Supplementary Material: Yes. Code.
Relation To Broader Scient... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s thoughtful and constructive feedback. We respond to each of the comments and concerns below.
1. ***"Generalization of the patterns"***
We evaluate our method across a broad range of multimodal tasks and VLMs, including open-domain question answering, multiple... | null | null | null | null | null | null | null | null |
Permutation Equivariant Neural Networks for Symmetric Tensors | Accept (poster) | Summary: The paper studies permutation equivariant models on symmetric tensors. It presents two characterizations of all linear permutation equivariant functions between symmetric spaces. Subsequently, the paper offers methods for generating the basis of these transformations and performing them in a memory-efficient m... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful and positive critique of our work. We are delighted that they recognise the “novel contributions” that are contained in our paper; particularly, the characterisation of the linear permutation equivariant layers for symmetric tensors and the map label notatio... | Summary: The paper derives an equivariant weight matrix for symmetric tensors, for example, a covariance matrix. They found the condition of permutation equivariant and expressed it into two bases: the orbit basis and the diagram basis. The diagram basis is more efficient to compute, although both compose the same spac... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing a positive critique of our work. We are pleased that the reviewer recognises that our work “invented a permutation equivariance for symmetric tensors, which had never been tackled before”, and found our theoretical and empirical evidence “convincing”.
Given tha... | Summary: This work introduces an exact characterization of all linear permutation equivariant functions between symmetric power spaces. The authors introduce the map label notation, which makes it possible to express a transformation as a series of equations, thereby eliminating the need to store the weights explicitly... | Rebuttal 1:
Rebuttal: We thank the reviewer for their critique of our work. We are pleased that they recognise that our work is “the first […] to obtain an exact characterization of permutation equivariant linear functions applied to symmetric tensors”. This aligns with the opinions of all of the other reviewers. We al... | Summary: This paper introduces permutation equivariant neural networks for symmetric tensors. In particular, this paper provides two different characterization of all linear permutation equivariant layers between symmetric tensors: orbit basis and diagram basis. Of these two characterization, the diagram basis leads to... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive review. We are pleased that they recognise that our work “[p]rovides equivariant network design for a new and relevant problem”, and “provides a complete characterization of the linear space with the constraint of permutation equivariance for symmetric ... | null | null | null | null | null | null |
Fundamental Limits of Visual Autoregressive Transformers: Universal Approximation Abilities | Accept (poster) | Summary: This paper shows that single-head, single-layer VAR transformers are universal approximators for Lipschitz image-to-image mappings, enabling them to approximate continuous transformations. This establishes their theoretical expressiveness and sets a new image synthesis benchmark, outperforming methods like Dif... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and recognition of our theoretical contributions. We appreciate your detailed feedback and would like to address the weaknesses you highlighted:
### Weakness 1: On connecting theory to practical applications
You raise an important point about establishing a cle... | Summary: The paper examines the fundamental limits of Visual Autoregressive (VAR) transformers, proving that single-head VAR transformers with a single self-attention layer and single interpolation layer are universal approximators. By adapting the established techniques in function approximation and neural network to ... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our paper. We appreciate your recognition of the theoretical contributions and clear organization of our work.
Regarding your question about designing experiments to demonstrate VAR Transformers as universal approximators for image-to-image tasks: This is ... | Summary: This paper aims to understand transformer-based models in image generation focusing on Visual Autoregressive Transformers (VAR). Transformers have already been shown to be universal approximators in certain settings (e.g., language tasks via prompt tuning [1]), but it is not clear if its visual counterpart (VA... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer for these insightful comments, and we would like to address the reviewer’s concerns as follows.
### Claims And Evidence: On the broader implications claim
We acknowledge that our claim regarding broader implications for CV could be better supported. Our inten... | null | null | null | null | null | null | null | null |
EA-PS: Estimated Attack Effectiveness based Poisoning Defense in Federated Learning under Parameter Constraint Strategy | Reject | Summary: This paper proposes a client-side defense methond in federated learning, EA-PS, that constrains the pertubation range of local parameters while minimizing the impact of attacks by forming the problem into an optimization problem. This paper further provides convergence and robustness analysis. This paper valid... | Rebuttal 1:
Rebuttal: Thank you for your thorough analysis and constructive feedback on our paper. We appreciate the opportunity to clarify the points raised and to provide additional insights into our research.
> 1. What is the definition of long-lasting attacks? Why $A_t - A_{t-1}$ can be interpreted as long-lastin... | Summary: To combat persistent adaptive attacks, the authors propose EA-PS, a client-side defense that enhances server-side methods for robust, stable performance. By limiting attack impact and constraining local parameter perturbations, EA-PS mitigates backdoor poisoning. Theoretically, it achieves a lower upper bound,... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our work.
> 1. The communication overhead should be compared to pure server-side defense.
Response: We'd like to address the concern regarding the communication overhead in our work. Since nothing but the parameter constraint strategy ... | Summary: This paper proposes EA-PS (Estimated Attack Effectiveness-based Poisoning Defense with Parameter Constraint Strategy), a client-side defense designed to constrain the perturbation range of local parameters while minimizing the impact of attacks.
The authors prove that our methods have an efficiency guarantee w... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work and for your insightful comments.
> 1. Table 1 caption is not quite clear benign accuracy / （attack success rate）？
Response: The metric used in Table 1 is backdoor accuracy, which is the attack success rate for backdoor attacks. We will change it to "... | null | null | null | null | null | null | null | null |
How Expressive are Knowledge Graph Foundation Models? | Accept (poster) | Summary: The manuscript introduces a Knowledge Graph Foundation Model (KGFM) termed MOTIF, which extends ULTRA’s relation graph into a relational hypergraph using manually defined motifs to incorporate additional information for computing a relation’s conditional representation. The authors theoretically establish a co... | Rebuttal 1:
Rebuttal: We note that TRIX is a **contemporaneous work** first published on 16 Nov 2024, i.e. **within 4 months** of ICML submission. We nevertheless provide answers to each of the raised concerns.
---
**Claims And Evidence**
1. TRIX [1] is the first to provide an expressivity analysis and we will acknow... | Summary: This paper presents a modified design for existing KGFMs, incorporating arbitrary motifs rather than being limited to binary motifs, as in previous approaches. This enhancement increases expressive power. Synthetic and real-world experiments validate the proposed improvements.
Claims And Evidence: Yes. Howeve... | Rebuttal 1:
Rebuttal: > *“**Methods**: ...The problem formulation is overly general, ... ”*
The reason our problem formulation is deliberately general is that our primary motivation is to rigorously analyze and broadly improve the expressive power of existing KGFMs, including widely used models like ULTRA. Our theoret... | Summary: The authors in this paper introduce MOTIF, a framework for enhancing expressiveness of KGFMs. The authors have conducted a rigorous, theoretical study on the expressive power of KGFMs that have been designed to generalize to unseen KGs with different relational vocabs, making them highly useful for inductive l... | Rebuttal 1:
Rebuttal: We thank the Reviewer for considering ours a "strong contribution".
>*“**Methods.** Yes, but how much overhead does MOTIF introduce? Are these human-readable? Do MOTIFs scale to large-scale KGs without affecting the performance?”*
>*“Q1: How much overhead does MOTIF introduce?”*
This question i... | null | null | null | null | null | null | null | null |
Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo | Accept (poster) | Summary: This paper is about solving inverse problems in a training-free manner using latent denoising diffusion priors and sequential Monte Carlo methods. It proposes a novel probabilistic models that enables inference of the hidden states with latent diffusion priors given the observation.
Specifically, this probab... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort put into the review. We are encouraged that the reviewer found our approach interesting and the analysis sound. We address the reviewer’s comments in the following. All answers and suggestions will be incorporated in the paper.
**(1)**
> “The main cl... | Summary: The authors propose a sampling method based on Sequential Monte Carlo (SMC) in the latent space of diffusion models. The proposed approach leverages the forward process of the diffusion model to introduce additional auxiliary variables (e.g. noisy measurements), followed by SMC sampling as part of the reverse ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort put into the review. We are encouraged that the reviewer found our approach specifically and SMC in general promising. We address the reviewer’s comments in the following. All answers and suggestions will be incorporated in the paper.
**(1)**
> “Furt... | Summary: The work proposed a Sequential Monte Carlo based sampling algorithm for solving imaging inverse problems with latent diffusion model. The writing is clear and easy to follow.
Claims And Evidence: The claim is solid. Despite achieving the highest perceptual quality, this work suffers from a significant loss in... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort put into the review. We are encouraged that the reviewer appreciated LD-SMC perceptual quality and the evaluation part. We address the reviewer’s comments in the following. All answers and suggestions will be incorporated in the paper.
**(1)**
> “This... | Summary: This paper studies inverse problems using sequential Monte Carlo sampling in a latent space. Generative models are great priors for the inverse problems. Although diffusion models achieve great performance, due to their computationally expensive reverse process and sequential nature, leveraging diffusion model... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and effort put into the review. We are encouraged that the reviewer regarded our approach as *Interesting exploration* and overall appreciated it. We address the reviewer’s comments in the following. All answers and suggestions will be incorporated in the paper.
... | null | null | null | null | null | null |
Proto Successor Measure: Representing the Behavior Space of an RL Agent | Accept (poster) | Summary: The paper constructs a linear framework, "Proto Successor Measures", for classifying the space of Q functions, a generalization of successor features. The paper provides some theoretical results on PSM, showing how they can be learned from offline data and used for inference at test-time. Experiments show the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for providing detailed feedback on our paper. We would like to address the concerns raised by the reviewer:
> At best, further samples are needed from the environment to infer a reward function. This could be problematic especially in sparse environments. :
We agree with t... | Summary: The paper introduces Proto Successor Measure (PSM), a basis set to represent all possible behaviors of an RL agent in an environment. The key insight is that any valid successor measure must satisfy the Bellman flow equation. By rearranging the Bellman flow equation one gets an affine equation. Any solution to... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for providing detailed feedback on our paper. We would like to clarify all the questions raised by the reviewer:
> The inference for the linear weights involves solving a linear program with constraints. This is harder to solve than linear regression in prior works. :
... | Summary: The paper investigates representation learning in RL with the aim of performing zero-shot learning: computing optimal policies on downstream tasks wihout any further training. Building on earlier works about representation learning in a reward-free setting, especially on successor representations, it proposes ... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking time to review our paper and providing detailed feedback. We would like to address the concerns of the reviewer below:
> I think the "mathematically complete" mention is a bit exaggerated here. The paper would require a much more quantitative analysis to deserve t... | Summary: This paper studies the reward-free RL problem and proposes the concept of proto successor measure (PSM), which is built on the idea of proto value functions in (Mahadevan and Maggioni, 2007) and serves as the basis set of all the possible visitation distributions in a given RL environment. By learning this bas... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed feedback. We would like to address all the concerns of the reviewer below:
> It would be good to compare a more diverse set of reward functions for each environment.:
We appreciate the reviewer’s suggestion and agree that our original evaluation using fo... | null | null | null | null | null | null |
RISE: Radius of Influence based Subgraph Extraction for 3D Molecular Graph Explanation | Accept (poster) | Summary: The paper proposes a new instance-level method to “explain” the predictions of 3D molecular GNNs. Following earlier work, this is formulated as an optimization problem over subgraphs, where the objective is to minimize the loss of predictive power when removing edges, under a given budget of edges. The author'... | Rebuttal 1:
Rebuttal: Thank you so much for your detailed and constructive comments! We provide our responses here.
## Claims And Evidence
> Interpretability of explanation
- The radii of influence can be intepreted as **the spatial extent within which an atom can significantly affect its surroundings**.
> Additiona... | Summary: This contribution introduces RISE (Radius of Influence based Subgraph Extraction), an innovative explanatory approach for 3D geometric Graph Neural Networks (GNNs) in molecular learning.
RISE's principal contribution is the allocation of a "radius of influence" to each atom (node). This delineates the confin... | Rebuttal 1:
Rebuttal: Thank you so much for your efforts reviewing our work. We respond to your questions below.
## Questions For Authors:
> Extension to larger systems with long-range interactions
- Even in larger atomic systems, short-range interactions typically dominate chemical bonding and molecular stability. Cov... | Summary: This paper proposes a novel explanation method that localizes interpretability within each node’s immediate 3D neighborhood. By defining a "radius of influence," the approach constrains message passing to spatially and structurally relevant subgraphs. This enhances interpretability while maintaining alignment ... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback! We provid our responses below.
## Experimental Designs Or Analyses
> Comparison with baselines on 2D GNNs
- **First of all, we want to emphasize that our work is not just an extension of its 2D counterparts.** Our work reformulates 3D graphs as direc... | Summary: The paper presents RISE, a method for explaining 3D molecular GNNs by identifying key substructures using a radius of influence for each atom. RISE formulates the explanation process as an optimization problem that finds a compact, chemically interpretable subgraph while maintaining prediction fidelity. Instea... | Rebuttal 1:
Rebuttal: Thanks for reviewing our work. We respond below.
## Claims And Evidence
- The baselines are given budgets **preserving more edges than RISE, favoring them in comparison**.
- Despite this, RISE **consistently** shows strong performance.
> Baselines old; existing methods ineffective on 3D graph
... | null | null | null | null | null | null |
Continual Reinforcement Learning by Planning with Online World Models | Accept (spotlight poster) | Summary: In this work the authors propose a new task-unknown continual reinforcement learning setting, in which the agent needs to learn a sequence of tasks without exactly boundary or id. To deal with this new setting, the authors propose a new continual RL method, OA, which introduce a sparse world model by FTL model... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and questions. Below we respond to the comments and raised questions.
***Methods And Evaluation Criteria***
> What does "task changes" in Algorithm A.2 mean and how does the agent know this?
Admittedly, the presented algorithm still requires the task boundary ... | Summary: The paper presents a Follow-The-Leader-based online world model, implemented as a composition of a learnable linear layer and a fixed-weight non-linear layer, that is used to solve the continual reinforcement learning problem as a part of model predictive control. The world model has a regret bound of $\mathca... | Rebuttal 1:
Rebuttal: Thank you for your valuable review and questions. Below we respond to the comments and raised questions.
***Claims And Evidence***
> The claim that "image-based environments...
We agree that the Atari games were solved even back in 2015. However, it typically takes millions to billions of simul... | Summary: In this paper, the authors focus on the problem of Continual Reinforcement Learning, solving multiple tasks that are presented in sequence. In practice, this is a difficult problem because conventional methods often lead to Catastrophic Forgetting. To this end, the authors propose a model-based agent that lear... | Rebuttal 1:
Rebuttal: Thank you for your supportive review, as well as the suggestions that we should evaluate the method on additional datasets/benchmarks. The main challenge of doing so is that there is no appropriate continual RL test suite to our knowledge. This is also the motivation for us to discuss the importan... | Summary: Update after rebuttal:
I have read the author responses and reviews. I would like to maintain my score recommending acceptance.
Summary:
The paper proposes an approach to Continual Reinforcement Learning (CRL) through the development of an Online Agent (OA) that leverages online world models. The central ... | Rebuttal 1:
Rebuttal: Thank you for your supportive review and questions. Below we respond to the comments and raised questions.
---
***Q1: What is meaningful overlapping referred to?***
We intended to mean the overlapping of task attributes. For example, two Atari games may lack meaningful overlapping because they ... | null | null | null | null | null | null |
Evaluating VLMs' General Ability on Next Location Prediction | Reject | Summary: This paper introduces a benchmark for evaluating the performance of vision-language models (VLMs) on next-location prediction. The benchmark is created with open-source map public taxi trajectory data. They draw the first 12 points of the taxi trajectory on the map and ask the VLMs to predict the location of t... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We appreciate the time and effort you have devoted to reviewing our work. Below, we address your concerns in detail.
**How much value does this new benchmark provide?**
1. **From the perspective of large vision-language models (VLMs)**:
The proposed bench... | Summary: This paper explores the general capability of Vision-Language Models (VLMs) in performing next-location prediction, a key aspect of spatial intelligence that humans often handle through visual estimation. The authors introduce VLMLocPredictor, a novel benchmark designed to evaluate VLMs' predictive capabilitie... | Rebuttal 1:
Rebuttal: Thank you for your comments. Below, we respond to your concerns. Due to space constraints, some points may be addressed briefly; please feel free to raise any questions.
**On the Superior Performance of Claude Models**
The Claude series consistently achieves SOTA results on spatial reasoning tas... | Summary: This paper introduces a new task, next-location prediction, which leverages map images and historical coordinates to predict the next location. The paper proposes a framework, VLMLocPredictor, which guides VLMs to iteratively refine the next-location prediction. Moreover, the paper compares the performance of ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. We appreciate the time and effort you have devoted to reviewing our work. Below, we respond to your concerns in detail.
### **Regarding Experimental Design**
1. While it is true that taxi drivers' trajectories are influenced by intent, prior work suggests th... | null | null | null | null | null | null | null | null |
AEQA-NAT : Adaptive End-to-end Quantization Alignment Training Framework for Non-autoregressive Machine Translation | Accept (poster) | Summary: The paper presents AEQA-NAT, a novel Non-Autoregressive Machine Translation (NAT) framework that introduces a Semantic Quantization Space (SQS) inspired by VQ-VAE. The key components include:
- Pre-aligned Semantic Quantization Space (SQS) leveraging mBART.
- Semantic Quantization Alignment Loss (LSQA) to enfo... | Rebuttal 1:
Rebuttal: Thank you for your comments. Your feedback has been very helpful to us.
1. The cost of aligned reordering is $O(n \cdot m \cdot d)$, and we do not use an additional aligner model during the inference phase. We appreciate your interest in the intuition behind our method. When we recognized the ex... | Summary: Non-autoregressive transformers (NATs) are attractive due to computational efficiency for machine translation workloads. However, existing approaches fail to completely close training-inference mismatches for these systems. This work proposes Adaptive End-to-End Quantization Alignment Training for NATs (AEQA-N... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments. Your feedback is important to us.
1. In response to your suggestion, we have added further descriptions to the sequence of expressions in Section 2.3 to more clearly explain the data flow process:
- The source text $(x_1,x_2, \dots, x_n)$ is input into the... | Summary: This paper works on non-autoregressive machine translation. It bridges the gap by introducing the latent variables and applying glancing training over the latent codes. In addition, order alignment of latent code is also introduced. The empirical results are very good.
Claims And Evidence: The main claim is t... | Rebuttal 1:
Rebuttal: Thank you for your comments.
## 1. Clarification on Motivation-Method Alignment
To address your concern regarding the alignment between our motivation and the proposed method, we would like to clarify the following: We highly value the role of GLAT in NAR systems, which is why we chose it as our ... | Summary: This paper argues that there is a training-inference gap in Non-autoregressive Transformers (NATs), where NATs sample target words during training to enhance input but have no access to target information during inference. To address this, they propose an Adaptive End-to-end Quantization Alignment (AEQA) train... | Rebuttal 1:
Rebuttal: Thank you for your comments. Your suggestions have been very helpful to us.
1. In the revised manuscript, we have added citations to the relevant literature you mentioned and revised the conclusions in both the abstract and the experimental sections accordingly.
2. In response to your suggestion,... | null | null | null | null | null | null |
Competing Bandits in Matching Markets via Super Stability | Accept (poster) | Summary: The paper identifies a problem with using the Gale-Shapely algorithm for stable matching with two-sided uncertainty: finding a weakly stable matching based on partial ranking doesn't give guarantees for the full ranking. Instead, they build on a different algorithm: finding a super stable ranking that guarante... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable review. Please find the responses below.
**Re motivation for regret instead of pure-exploration:** In online learning (including matching markets), guaranteeing good cumulative performance and minimizing losses is crucial. Regret, unlike pure exploration, ... | Summary: This paper studies bandit learning in two-sided matching markets where both users and arms have unknown preferences and must learn them through bandit feedback. It introduces super-stable matching, using Irving’s (1994) concept to overcome the limitations of standard Gale-Shapley (GS) algorithms, which only gu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback. We first want to clarify the paper's scope.
This paper advances bandit learning for finding *any* stable matching in matching markets. It demonstrates Extended-Gale Shapley's effectiveness over standard Gale Shapley and uses super-stable matching ... | Summary: This paper studies the bandit learning in matching markets with two-sided unknown preferences. It investigates the structure of super stability to determine the exploration-exploitation process. Existing works mainly consider LCB-UCB methods before identifying the full ranking or using known $\Delta$ to decide... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments.
**Relationship between $\Delta_{\min}$ and $\Delta_{\mathcal{A}}$:**
We first note that for the partial rank where the top $N$ user for each arm, and the top $N$ arms for each user are separated we always have the user-optimal matching as a super... | Summary: This paper addresses the problem of bandit learning in two-sided matching markets with two-sided reward uncertainty, where both users and arms must learn their preferences through repeated interactions. The authors propose an innovative approach using super-stability from Irving (1994) to enhance traditional G... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review.
**Re How will you make this fully-decentralised?**
The binary flags are used to set *restart* to True and *success* to False by individual user or arm. Note that *restart* is set from the user side, and arm side only passively acts on the *restart*... | null | null | null | null | null | null |
A Comparison of LLM fine-tuning Methods & Evaluation Metrics with Travel Chatbot Use Case | Reject | Summary: This paper compares various finetuning and evaluation metrics of LLMs, focusing on the travelling dataset. With RLHF, this paper claims Mistral 7B achieves better performance than GPT-4.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: The authors used Reddit dataset to perform RLHF. However, the R... | Summary: This paper compares various LLMs' fine-tuning methods and evaluation metrics in the context of a travel chatbot. The study evaluates three fine-tuning approaches: QLoRA RAFT, and RLHF, applied to two 7B-parameter LLMs, LLaMa 2 and Mistral. The dataset, sourced from Reddit travel-related subreddits, was augment... | Summary: Summary:
The paper mainly focus on the comparison of various fine-tuning methods (QLoRA, RAFT, RLHF). Two pre-trained LLMs (LLaMA 7B & Mistral 7B) were fine-tuned and the performance was evaluated against various metrics. Besides, the author collect the travel dataset from travel-related subreddits and find th... | Summary: This paper compares LLM fine-tuning methods (QLoRA, RAFT, RLHF) and evaluation methods (E2E benchmarks, NLP metrics, Ragas, GPT-4 metrics, and human evaluation) using a travel chatbot case. Data was sourced from Reddit and augmented for each fine-tuning method. QLoRA and RAFT were applied to LLaMA2-7B and Mist... | null | null | null | null | null | null | ||||
Boosting Adversarial Robustness with CLAT: Criticality Leveraged Adversarial Training | Accept (poster) | Summary: The paper presents CLAT, a layer-aware adversarial training algorithm designed to mitigate adversarial overfitting by identifying and fine-tuning layers that learn non-robust features. The approach leverages layer criticality, a metric that quantifies a layer’s functional importance to subsequent features, to ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and suggestions. We address each point below.
**Finetuning from beginning / CLAT for training:** CLAT can indeed be applied from the beginning, without any prior training, as well as after standard adversarial training. This directly addresses your suggesti... | Summary: The paper aims to make adversarial training more efficient by selectively training only the most critical layers based on a criticality factor. This factor is determined using the local Lipschitz constant, calculated as the average difference in a layer's features with and without adversarial perturbations add... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for recognizing the experimental rigor of our work. Below, we respond to each of your concerns and questions.
**Novelty:** Please see the “Novelty” section in our response to Reviewer **8QH8**. For a concrete comparison, consider RiFT [1], a fine-tuning ... | Summary: The paper introduces CLAT (Criticality-Leveraged Adversarial Training), which aims to enhance the adversarial robustness of neural networks by identifying and fine-tuning critical layers that are most vulnerable to adversarial attacks. The main contributions include the development of a criticality index for l... | Rebuttal 1:
Rebuttal: Thank you so much for your time! Below, we’ve provided detailed responses to each of your concerns and criticisms. We hope this helps clarify everything.
**Experimental support and breadth of evaluation:**
Respectfully, we disagree with the concern regarding insufficient experimental support. A... | Summary: This paper proposes a criticality index to identify critical layers that are more prone to perturbation and then apply CLAT to fine-tune these layers for better clean and adversarial accuracy. Results are evaluated on various models, methods and datasets, proving the effectiveness of CLAT.
Claims And Evidence... | Rebuttal 1:
Rebuttal: We’re grateful for your insightful comments and for appreciating the care we put into our experiments. Below, we offer detailed responses to each of your points.
**Marginal improvement:** We respectfully disagree. Gains of ~2% in adversarial robustness—particularly through fine-tuning—are conside... | null | null | null | null | null | null |
Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language Models Through Logic Unit Alignment | Accept (poster) | Summary: This paper introduces Reasoning-as-Logic-Units (RaLU), a novel test-time reasoning framework designed to address hallucinations in LLM reasoning and enhance their performance in mathematical and coding reasoning tasks.
Specially, RaLU consistes of three parts.
Logic Unit Extraction: begins by generating an i... | Rebuttal 1:
Rebuttal: Thank you for the review. To ensure we address your concerns with precision, could you kindly clarify your suggestions? We are delighted to address any questions you may have and refine our paper accordingly. We look forward to your feedback and reassessment. | Summary: This paper presents a novel test-time scaling framework, Reasoning-as-Logic-Units (RaLU), which consists of three main steps: Logic Unit Extraction (directly generating a program to address the given problem, and using static analysis tools to create a control flow graph to decompose the program into logic uni... | Rebuttal 1:
Rebuttal: We are sincerely grateful for your recognition of RaLU’s contributions. Many thanks to your constructive comments to enhance our work.
# Questions
We appreciate the reviewer's insightful question.
Let’s use perplexity=exp(-1* Mean(token probabilities)) as another metric using “multiplying”. We h... | Summary: This paper introduces a novel prompt engineering-based approach, Reasoning-as-Logic-Units (RaLU), which consists of three key components:1) Logic Unit Extraction, 2) Logic Unit Alignment, and 3) Solution Synthesis, to enhance the reasoning capability of the LLMs.
RaLU decomposes the task into multiple logic u... | Rebuttal 1:
Rebuttal: Thank Reviewer KWVa for your constructive feedback on our work. We have carefully considered all comments and hope our point-by-point response can address your questions.
## Questions
1. **Multiple runs of experiments**: Thank you for your feedback! Given the inherent stochastic nature of LLMs a... | Summary: The paper proposes a novel prompting/structured reasoning technique method (RaLU) that mitigates reasoning inconsistencies within the generated LLM output by proposing an alignment (alignment between the task and the generated code) and self-refinement (decomposing code into logical units and iteratively refin... | Rebuttal 1:
Rebuttal: We are grateful for your valuable comments and hope this response can address your questions.
# Questions
1. We select the latest models from three renowned open-source families. The effectiveness of RaLU is not directly tied to the model's size but rather depends on the model's reasoning capabil... | null | null | null | null | null | null |
DEALing with Image Reconstruction: Deep Attentive Least Squares | Accept (poster) | Summary: This paper introduces Deep Attentive Least Squares (DEAL), a novel data-driven image reconstruction method that bridges traditional regularization techniques with modern deep learning. The authors propose an alternative to complex, highly parameterized deep architectures by leveraging the principles of classic... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s time and feedback and are glad that DEAL is recognized as creative and original. To summarize our responses:
- We added a paragraph to the related work focusing on attention mechanisms.
- We performed a new experiment to survey the scalability of DEAL for large medica... | Summary: This paper presents a least-square-type image reconstruction method. It is formulated as a conjugate gradient method. It consists of of an iterative refinement process with two main components: one that estimates the reconstructed image and a another that generates a mask, modulating the response of the prior ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's time and feedback and are glad that DEAL is recognized as a significant improvement in the performance-efficiency trade-off within image reconstruction techniques. In summary,
- We clarified our choice of comparison methods.
- We added diffusion models and transformer... | Summary: This paper presents Deep Attentive Least Squares (DEAL), a novel image reconstruction method that bridges traditional signal processing and modern deep learning. DEAL formulates reconstruction as an iterative least squares problem with spatially adaptive regularization, where an attention mechanism dynamically... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s feedback. We added more experiments and comparisons to highlight DEAL’s generalization and scalability (see response point 2 to reviewer dGc6 and response point [Questions For Authors] to reviewer Q6VC) and provided additional details on the attention mechanism (see re... | Summary: Summary
This paper proposes a novel Maximum a posteriori (MAP) method for solving linear inverse problems with Gaussian noise. The proposed method is based on Fields-of-Experts (FoE) regularization [1]. The authors suggest using quadratic potentials and learning the remaining parameters of the FoE regularizat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and valuable comments. To summarize our response:
- We addressed the discrepancy of our numbers with the DPIR paper (due to cropping), we also added denoising results for the DPIR setup to underline the validity of our results.
- We compare with a state-of-the-... | null | null | null | null | null | null |
Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners | Accept (poster) | Summary: This paper introduces D-BETA, a cross-modal pre-training framework designed for self-supervised learning of ECG signals and textual reports. D-BETA combines the strengths of generative and contrastive learning by leveraging masked language modeling and masked ECG reconstruction to recover missing data. Additio... | Rebuttal 1:
Rebuttal: > [R4-1]: Lack of originality / Limited originality: The framework combines multiple loss functions (ETS, ETM, MLM, MEM), none of which are original contributions by the authors. Furthermore, the coefficients used to balance these losses are not systematically explored or justified. This makes th... | Summary: This paper presents a self-supervised pretraining method for jointly learning from electrocardiograms (ECGs) and text. Their method, D-BETA, combines modality-specific masked modeling, a sigmoid matching loss, and a nearest neighbor negative sampling strategy to enhance performance. Results demonstrate superio... | Rebuttal 1:
Rebuttal: > [R3-1]: "Were baselines pretrained from scratch on the same data as D-BETA, were their model weights taken as is and used for fine-tuning, or were results taken directly from their respective papers?"
Regarding baseline comparison, we used the results reported in the original baseline papers. T... | Summary: This paper introduces the D-BETA framework for joint pre-training of ECG signals and their corresponding clinical text reports, aiming to learn cross-modal self-supervised representations. The method integrates generative tasks—specifically, masked language modeling (MLM) and masked ECG reconstruction (MEM)—wi... | Rebuttal 1:
Rebuttal: > [R2-1]: "The overall task design seems to largely build upon existing multimodal joint modeling methods and appears more like an aggregation of several tasks, lacking sufficient novelty."
While our framework builds upon several established tasks, to the best of our knowledge, **we are the first... | Summary: This paper proposes D-BETA, a novel contrastive masked transformer-based architecture to pre-train ECG signals and corresponding texts. The key components of the proposed approach include self-supervised learning for both ECG and medical texts, as well as fusion mechanism for both to enhance cross-learning. A ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback on our submission. We would like to address your comments below:
> [R1-1]: “In some scenarios, the text info, especially from doctors, may be seen as a ground truth or label, rather than training data. Some discussion on this would be helpful,... | null | null | null | null | null | null |
Optimization for Neural Operators can Benefit from Width | Accept (poster) | Summary: This paper proposes a unified optimization framework using Restricted Strong Convexity (RSC) and smoothness to establish gradient descent convergence guarantees for Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs). The key contributions are as follows:
1. A theoretical proof that the empirical... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to review our paper.
---
We start by responding to the reviewer's concerns.
- We respectfully state that our work is not a "limited novel extension" of (Banerjee et al., 2023a): for a detailed justification we refer to our response to Reviewe... | Summary: This paper addresses the problem of optimization convergence guarantees for neural operators, specifically Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs), when trained using gradient descent (GD). The authors propose a unified optimization framework based on two key conditions: restricted s... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to review our paper and for the list of questions which we now address.
- **Question 1**: **First subquestion:** Our theoretical work establishes sufficient conditions for optimization and shows that the convergence rate may benefit from width, ... | Summary: The main results of this paper are to derive optimization convergence results for both Deep Operator Networks (DONs) and Fourier Neural Operators (FNOs), under gradient descent (GD). The main techniques in this paper are to show that the empirical loss functions for such two kinds of networks satisfy Restricte... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to review our paper. We now address the points raised by the reviewer.
We are grateful for the question of guaranteeing convergence for the whole training procedure. If we understood correctly (and we kindly ask the reviewer to correct us if we ... | Summary: The paper provides convergence guarantees for neural operator learning, which are valid under assumptions of restricted strong convexity and smoothness of the loss function. The authors demonstrate that two learning operators (DON and FNO) satisfy these conditions. Both theoretical and experimental findings sh... | Rebuttal 1:
Rebuttal: We are grateful to the reviewer for taking the time to review our paper. We now respond to the reviewer's concerns.
---
We respectfully argue that our work **is not incremental** to (Banerjee et al., 2023a) [using the reference as cited in our work] for three reasons:
1. Our work **generalizes*... | null | null | null | null | null | null |
TTFSFormer: A TTFS-based Lossless Conversion of Spiking Transformer | Accept (poster) | Summary: The work presents a strategy to convert trained ANN transformer models into time-to-first-spike coded SNNs. Specifically, a neuron dynamics model with two flexible kernel functions is used to accurately represent all transformer model operands. It is shown that crucial operations such as SiLU/GELU activation f... | Rebuttal 1:
Rebuttal: Thanks for your suggestions. We would like to address your concerns and questions in the following.
### Code implementation
Thank you for your question. Our code will be released with the publication.
### Is there a way to quantify the minimum processing latency of a TTFSFormer neuron block?
T... | Summary: This paper proposes TTFSFormer, a novel method for converting Transformer architectures into Spiking Neural Networks (SNNs) using Time-to-First-Spike (TTFS) coding. The key innovation lies in designing generalized spiking neurons that address the limitations of prior TTFS-based approaches, particularly their i... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback. We are encouraged that you found our work novel with good results. We would like to address your concerns and questions in the following.
## 1. Table 1 lacks comparisons to very recent TTFS-based methods or advanced rate-coding SNN Transformers.
Thanks for y... | Summary: The paper introduces a novel approach, TTFSFormer, for converting Transformer architectures into Spiking Neural Networks (SNNs) with Time-to-First-Spike (TTFS) coding. The method addresses the challenge of preserving high accuracy while significantly reducing energy consumption. The authors propose new neuron ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful comments. We would like to address your concerns in the following.
### Hardware Implementation
Thanks for your question. Since rate-based SNN is currently the most popular coding method, existing neuromorphic chips are specially designed for rate-based algorithms. W... | Summary: This paper propose an ANN-SNN conversion method for spiking transformer based on time-to-first-spike (TTFS) method. The author first analyze the limitations of the previous TTFS method, and then propose a generalized TTFS neuron, which make it easier to relate Transformer to its SNN version. Experimental resul... | Rebuttal 1:
Rebuttal: Thank you for your positive and thoughtful comments. We are encouraged that you find our idea impressive. We are glad you agree that our method achieves SOTA performance with low energy consumption. We would like to address your concerns and questions in the following.
### 1. Please clarify how t... | null | null | null | null | null | null |
Network Sparsity Unlocks the Scaling Potential of Deep Reinforcement Learning | Accept (oral) | Summary: The paper builds upon previous work on scaling model size in RL [1], and extends its limit with simple layer-wise random pruning at initialization [2]. This is primarily verified in state-based RL (DMC), where a pruned large network greatly surpasses a dense network with the same number of trainable parameters... | Rebuttal 1:
Rebuttal: Thank you for your positive review. We address your questions below.
> Q1: Related work on iterative pruning
We have already discussed relevant works on dynamic sparse training (DST) in RL within our Introduction and Related Work sections. In the next version, we plan to expand our coverage of m... | Summary: This paper explores the scalability benefits of incorporating static network sparsity in deep reinforcement learning models. It introduces one-shot random pruning, where a fixed proportion of network weights are removed before training, leading to improved parameter efficiency compared to scaling up dense arch... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and positive evaluation of our work.
> It would be interesting to see these observations extended to value-based methods in discrete action spaces, such as DQN on Atari. Nonetheless, the current findings are valuable and worth sharing with the community.
We agr... | Summary: This paper uncovers an interesting finding: Instead of pursuing more complex modifications, introducing static Network sparsity alone can unlock further scaling potential beyond their dense counterparts with state-of-the-art architectures. And in experiments, they show that only using one-step random pruning ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive evaluation of our work.
> Q: What would happen to the learning ability when you only prune the critic or actor? Does a sparse actor play a key role?
This is an excellent question about the relative importance of sparsity in different components of actor-crit... | Summary: This paper shows that current deep RL architectures result in performance decreases when scaling the network in width or depth. Introducing sparsity in the form of a fixed mask over the weights, is able to to resolve this issue. The source of the benefits of sparsity is investigated in terms of representationa... | Rebuttal 1:
Rebuttal: > **Q1**: Limited DMC environments affecting generalizability
We acknowledge the reviewer's concern that most experiments are conducted on DMC, which might impact the generalizability of our analyses. To address this, we've conducted new experiments on the Atari-100k benchmark using Data Efficien... | null | null | null | null | null | null |
Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency | Reject | Summary: This paper analyses the variance of pixel intensity over generation timesteps for salient versus non-salient regions of the image. They call the rate of change the generation rate. Specifically, they find that the salient regions of the image generally have a higher variance than non-salient points (86% of the... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Below we address the questions.
**Q1. Larger Dataset for Visual Saliency Evaluation.**
We have expanded our visual saliency evaluation to include the full MIT saliency benchmark CAT2000, consisting of 2000 diverse-category images. Our results indicate that 81%... | Summary: This work analyzes the correlation between image features relevant to visual saliency and the local deformation of the data manifold induced during the reverse diffusion process, referred to as the generation rate. Empirically, the authors find that the generation curve—the ordered sequence of generation rates... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Below we address the questions.
**Q1-1. Expanded Discussion on Baselines - Object Removal.**
Methods for object removal generally fall into two categories: image inpainting and instruction-based editing (as detailed in the recent survey [Huang2025]). We select... | Summary: This work researches the visual properties of images during the diffusion process. By employing the manifold hypothesis, the authors propose a new metric called the generation rate. They experimentally show correlation of this metric with the visual properties of image generation. Furthermore, the authors desi... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Below we address the questions.
**Q1.Theoretical Justification of Contractive $D_{f_t}$.**
The observation of contractility is primarily empirical, demonstrated through experiments where tangent vectors {$v_i$} are scaled under the forward differential $D_{f_... | Summary: Motivated by differential geometry of manifold, the authors defined a metric called (projected) generation rate. It signifies how much a direction on tangent space of the manifold at time t were amplified or diminished through the reverse diffusion mapping. They manage to compute it locally for a patch of pixe... | Rebuttal 1:
Rebuttal: Thank you for your detailed review! Below we address the questions.
**Q1. Continuous-Time Formulation of Generation Rate**
We agree that leveraging a continuous-time formulation provides a scheduler-independent definition. For the diffusion ODE $\frac d {dt}X_t=h(X_t,t)$, the variation of the no... | null | null | null | null | null | null |
Synthetic Text Generation for Training Large Language Models via Gradient Matching | Accept (poster) | Summary: This paper introduces GRADMM (Gradient Matching with ADMM), a novel method for generating synthetic, human-readable text to train large language models (LLMs) efficiently while preserving privacy. The approach leverages gradient matching to ensure that synthetic text replicates the training dynamics of real da... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our work and our thorough experiments.
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1. Unverified assumption $\xi \leq |g_t|$ in theory.
The following [figures](https://anonymous.4open.science/r/gradmm/grad_diff/) confirm the validity of our theoretical assumption, by showing tha... | Summary: This paper presents a novel approach for generating synthetic human-readable text to train Large Language Models (LLMs) via gradient matching. The authors propose a method called GRADMM (GRADient matching with ADMM) that leverages the Alternating Direction Method of Multipliers (ADMM) to iteratively optimize s... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the novelty of our method, our theoretically-rigorous framework, our well-supported claims and extensive experiments.
----
**Experimental validation**:
- Problem formulation: gradient matching in Eq 3 applies to both the data scarce regime (Fig 1) and Data... | Summary: This paper improves on the SOTA synthetic data for LLM method by imposing a readability constraint in (2). This makes it necessary to 4.2 Alternating Between Text and Embedding Spaces. The experiments are convincing.
Claims And Evidence: They are good
Methods And Evaluation Criteria: They are good
Theoretic... | Rebuttal 1:
Rebuttal: We thank the reviewer for supporting our work and acknowledging our convincing experiments.
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1. Can this scheme used to generate math, logic, and code?
The idea of our work (generating synthetic text via gradient matching using ADMM) can be applied to math, logic or code. However, this requ... | Summary: This paper discussed a method for generating synthetic data to train LLMs, and aims to create a synthetic dataset that can train a similar dynamics to the real data. Theories and experiments are provided to justify the effectiveness.
Claims And Evidence: I think the evidence is not very convincing, especially... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. However, we disagree with their evaluation of our work, as we discuss below.
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**Data scarce regime**: Fig 1 provides strong evidence for the applicability of our method to the data scarce regimes. The 100 synthetic data generated by GRADMM based on ... | null | null | null | null | null | null |
Learning from Loss Landscape: Generalizable Mixed-Precision Quantization via Adaptive Sharpness-Aware Gradient Aligning | Accept (poster) | Summary: This paper propose a search-based method to find mixed-precision quantization scheme that can work with a much smaller dataset. To enable the generalization from the small search set to the large validation set, the paper propose to seek for quantization scheme that can lead to flatter loss minima of the quant... | Rebuttal 1:
Rebuttal: ## Q1 The claims of ASGA advantages and search cost.
A1: Many thanks. We think the ambiguity of claim's presentation may cause your confusion of this work. Our experiments (**Table 1 in Section 3 of our paper**) show that **using ASGA with ResNet50 saves 16 GPU hours with better Top-1 compared to... | Summary: This paper aims to reduce the mixed-precision quantization search costs by decoupling the policy search and model deployment dataset. In this way, the mixed-precision quantization policy of a model can be searched on a small-scale dataset and then the policy can be transferred to a large-scale one for deployme... | Rebuttal 1:
Rebuttal: ## Q1 The novelty of the work.
A1: Many thanks for your comment! This is a valuable question. The method SAQ [1] you mentioned appears similar to our work, but **they are actually different**. First, the goal of our work is **fundamentally different** from such work. SAQ quantizes and trains targ... | Summary: In the paper, authors propose a novel mixed-precision quantization method (ASGA) via learning the sharpness of loss landscapes, which improves the quantization generalization across datasets, thereby reducing the search cost. Particularly, the idea of introducing sharpness measure into quantization is interes... | Rebuttal 1:
Rebuttal: ## Q1 The rationality of the theoretical assumptions in the article.
A1: Many thanks for your insightful comments! As you mentioned, our theoretical analysis employs certain assumptions, including the PAC-Bayesian framework and convergence analysis under stochastic optimization. We adopt PAC-Baye... | Summary: This paper proposes an Adaptive Sharpness-Aware Gradient Aligning (ASGA) method for generalizable mixed-precision quantization. ASGA aims to address the issue of excessive search burden in MPQ through quantization generalization, i.e., searching for quantization policies on small proxy datasets and then genera... | Rebuttal 1:
Rebuttal: ## Q1 The authors should briefly explain why σmax can measure the sharpness of the loss landscape.
A1: Many thanks for your comments! First, reference [1] has proven that $\sigma_{max}$ is positively correlated with the sharpness of the loss landscape. Moreover, $\sigma_{max}$ is the eigenvalue w... | null | null | null | null | null | null |
Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning | Accept (poster) | Summary: The paper studies the effect of token-wise reweighting on gradient ascent type unlearning algorithms. They consider two kinds of reweighing: importance-based and saturation-based reweighing. They find that saturation is often more effective than importance-based reweighing and assigns lower weights to data wit... | Rebuttal 1:
Rebuttal: Many thanks for your constructive comments and suggestions! Please see our responses below.
**Q1 The experiments are done without repetitions, and it is unclear whether SatImp has a marginal improvement.**
Many thanks for your suggestions. Here we provide the standard deviations for part of Tabl... | Summary: This paper studies the loss reweighting mechanism for gradient difference-based LLM unlearning methods. It investigates the effect of importance-based reweighting and saturation-based reweighting on TOFU-1% setting. Based on the observations, a new reweight mechanism, SatImp, is proposed, and the results of th... | Rebuttal 1:
Rebuttal: Many thanks for your constructive comments and suggestions! Please see our responses below.
**Q1 More experiment about Claims 1-4**
Thanks for the detailed review.
For Claim 1, we contain the relevant results in the Appendix Figure 13,14 (m)-(x). We will include the performance for SimImp in al... | Summary: This paper studies LLM unlearning. The authors investigate the loss reweighting mechanism for LLM unlearning, where each token in the forget set is assigned a different weight in loss calculation. Specifically, the authors propose two ideas for loss reweighting: saturation, which suggests that tokens that are ... | Rebuttal 1:
Rebuttal: Many thanks for your constructive comments and suggestions! Please see our responses below.
**Q1: Is SatImp an incremental version of WGA?**
We sincerely appreciate your comments, but we respectfully disagree with your assessment that SatImp is an incremental version. Before reading the respon... | null | null | null | null | null | null | null | null |
Reliable Algorithm Selection for Machine Learning-Guided Design | Accept (poster) | Summary: This paper proposed a method for design algorithm selection which combines designs' predicted property values with held-out labeled data to reliably assess whether a candidate design algorithm configuration produces successful designs. Specifically, the method selects configurations for design algorithms such ... | Rebuttal 1:
Rebuttal: We appreciate your feedback! We would like to refer the reviewer to the first 3 paragraphs of our response to reviewer EEWq, as we believe there has been a misconception regarding our primary contributions.
In particular, to our knowledge, our work is the first to formalize and propose a rigorou... | Summary: Hyperparameter tuning and algorithm selection can be really tricky in real scenes. This paper proposes a method based on prediction-powered inference techniques for design algorithm selection, aiming to choose some settings (configurations) that satisfy users' demands. Two practical experiments demonstrate thi... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We are glad the reviewer found the idea of selecting reliable algorithms to have strong potential to benefit the ML community. We believe there has been a misconception regarding our primary contributions.
**[First to formalize + address DAS]** To our knowledge, altho... | Summary: When performing model-guided design, the goal is to propose new objects x that have some desired property, where the relationship between x and the property is approximated by a predictive model f(x). The problem is that f(x) may be unreliably when proposing x far from the training data. This makes it difficul... | Rebuttal 1:
Rebuttal: We are grateful for the positive evaluation of our work, and are glad the reviewer found our method and experiments well-motivated and appealing. Our responses to specific comments and questions follow.
- "The hyper-parameter range is also discretized at an arbitrary resolution of 100 values, but... | null | null | null | null | null | null | null | null |
Towards Practical Defect-Focused Automated Code Review | Accept (spotlight poster) | Summary: The paper introduces an end‐to‐end automated code review system designed specifically for defect detection in large-scale, industrial codebases. The authors identify four key challenges in automating code review: capturing the full, relevant code context; improving key bug inclusion to ensure that critical def... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the thoughtful and encouraging feedback. We greatly appreciate your recognition of the system design, the practical orientation of our evaluation, and the paper’s contributions toward large-scale industrial defect detection. Below, we address your questions in... | Summary: The paper presents a language-model based system for automated code review. The methodology boils down to using static analysis tools to identify the most relevant parts of the code base, and then passing this through several LLM calls to generate the review, identify the key components, and filter out noise. ... | Rebuttal 1:
Rebuttal: **We sincerely thank the reviewer for the constructive and thoughtful feedback. We appreciate your recognition of the clarity of the paper, the strength of the experimental design, and the potential real-world impact of our work. Below, we respond to your questions and key suggestions.**
---
###... | Summary: This paper proposes an advanced method for automating code reviews, focused on defect detection and improving real-world code review workflows. To address the challenges, the authors introduce a multi-agent LLM framework that utilizes code slicing algorithms, a filtering mechanism to remove irrelevant comments... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the constructive and insightful feedback. We appreciate your recognition of our system’s architecture and the observed performance improvements on real-world data. Below, we address your concerns regarding generality, LLM dependency, filter sensitivity, and workflow int... | Summary: In this paper, the authors proposed a framework for automated code review. More specifically, the authors first used code slicing to enable the Multi-Agent Code Review System to obtain sufficient context fragments of the code. Then, the Multi-Agent Code Review System conducted code reviews, filters, aggregates... | Rebuttal 1:
Rebuttal: **We thank the reviewer for the constructive comments and the recognition of our framework’s practical impact. We have addressed the concerns by enhancing dataset transparency, adding new evaluations (including error category analysis and heterogeneous LLM roles), clarifying metric rationale, and ... | null | null | null | null | null | null |
BOOD: Boundary-based Out-Of-Distribution Data Generation | Accept (poster) | Summary: This paper focuses on addressing the OOD detection task by synthesizing OOD samples. To generate plausible OOD samples, samples near the OOD boundary are first selected and then perturbed along the direction of gradient ascent until their predicted labels change. Finally, a diffusion model is applied to genera... | Rebuttal 1:
Rebuttal: # Response to reviewer sEzZ
We thank the reviewer for the feedback and constructive suggestions. Our response to the reviewer’s concerns is below:
> Weakness 1: How can we ensure that during the generation from 0 to c, the original ID sample of class y is not mistakenly perturbed into another ID... | Summary: This paper studies the problem of out-of-distribution (OOD) detection for image tasks. The authors leverage text-to-image latent diffusion models to synthesize OOD images that are used to train the binary OOD detector. In doing so, they follow a three-step strategy: 1) Identifying the ID samples that are close... | Rebuttal 1:
Rebuttal: # Response to reviewer Mr32
We appreciate the review for providing valuable advice. Below are our responses:
> Claims and Evidence 1: there is no clear evidence showing that the method is truly efficient
We apologize for the inaccurate expression in the abstract: BOOD provides a more **training... | Summary: This paper introduces a framework called Boundary-based Out-Of-Distribution data generation (BOOD). BOOD synthesizes high-quality OOD features and generates outlier images using diffusion models.
The BOOD framework learns a text-conditioned latent feature space from the ID dataset, selects ID features closes... | Rebuttal 1:
Rebuttal: # Response to reviewer Smhu
> Essential References Not Discussed.
Thanks for recommendation for essential references. We will add them into our camera-ready paper.
> Weakness 1: the process of tuning hyperparameters may be challenging.
We appreciate the reviewer for the meaningful concerning. W... | Summary: This paper introduces a framework for generating synthetic out-of-distribution (OOD) data by explicitly targeting decision boundaries in latent feature space. The proposed method, BOOD, employs an adversarial perturbation strategy to identify in-distribution (ID) features closest to the decision boundary and p... | Rebuttal 1:
Rebuttal: # Response to reviewer wxMm
We thank the reviewer for the comments.
> Weakness 1: The compared methods are 2023 and before, how about the performance regarding to the most recent works?
Thank you for your concern regarding baseline methods. Please note that our work is based on using diffusion m... | null | null | null | null | null | null |
The Lock-in Hypothesis: Stagnation by Algorithm | Accept (poster) | Summary: This paper examines how feedback loops in human–LLM interactions can lead to belief lock-in, where dominant views become entrenched while conceptual diversity declines. The authors propose the lock-in hypothesis and support it with three approaches: (1) empirical analysis of the WildChat-1M dataset, showing a ... | Rebuttal 1:
Rebuttal: ### Summary of updates on WildChat analysis
| | gpt-4-turbo kink | gpt-3.5-turbo kink1 | gpt-3.5-turbo kink2 | user-wise regression |
|---------------------|------------------|---------------------|---------------------|----------------------|
| Lineage diversity | Negative ... | Summary: Paper proposes the "lock-in" hypothesis, and presents a series of empirical and simulated experiments, and theoretical analysis to provide evidence for the hypothesis.
Claims And Evidence: The main claim is that LLM/human interactions induce a feedback loop which forms echo chambers that leads to loss in dive... | Rebuttal 1:
Rebuttal: ### There is not enough detail in the main paper regarding the setup of the experiment"
All details requested here are either presented in the main text or appendix.
- "Structure of the collective knowledge base?" - This is in 4.1 under bold text "knowledge base". Some examples of knowledge bas... | Summary: This paper studies a very interesting problem, noted as the lock-in hypothesis, that during long-term development and evolution, language models’ topics and beliefs is reinforced by the user’s preference and feedback loop, effectively creating an echo chamber. The authors use the wildchat dataset through one y... | Rebuttal 1:
Rebuttal: Thank you for the feedback! We think the following results may help answer your question.
We find causal evidence of sustained diversity loss induced by model version updates, even after controlling for a range of confounders, testing 3 different diversity metrics, and selecting the subset of str... | null | null | null | null | null | null | null | null |
Chameleon: A Flexible Data-mixing Framework for Language Model Pretraining and Finetuning | Accept (poster) | Summary: This manuscript introduces a flexible data-mixing framework for LLM that uses kernel ridge leverage scores (KRLS) computed from learned domain embeddings using a proxy model. It quantifies each domain’s representativeness and interdependence within the embedding space, and then uses this information to generat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. Runtime analysis and complexity comparison
Obtaining embeddings $x_i, \, i=1,\ldots,k$ requires a single forward pass for each $a \in B_i$ through the proxy $h_{\theta_p}(a)$; inference is fast as t... | Summary: This paper introduces a new data mixing framework for language pretraining and finetuning wherein the mixing weights for different domains are constructed from a domain affinity matrix generated via kernel functions on domain embeddings. This domain matrix can naturally be transformed into domain weights for p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. Reasoning behind obtaining domain weights from the KRLS
We use kernel ridge leverage scores (KRLS) to determine domain weights. KRLS is a well-established tool in data analysis. It quantifies the in... | Summary: Authors propose a method for data sampling for pretraining and finetuning language models. Their idea is to train a classifier, then extract the middle layer word embeddings of the classifier for each domain in the training data, and then to do matrix factorization to obtain a scalar weight for each domain. Th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback and address all remaining concerns below:
> Q1. Theoretical motivation
We use kernel ridge leverage scores (KRLS) to determine domain weights. KRLS is a well-established tool in data analysis. It quantifies the influence or importance of data poi... | null | null | null | null | null | null | null | null |
Score Matching with Missing Data | Accept (oral) | Summary: The paper addresses the problem of parameter estimation from missing data with score matching objective. The authors introduce two general frameworks for estimating the (gradient of) marginal score and demonstrate strong empirical performance on synthetic and real data.
Claims And Evidence: The work provides ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and feedback as well as the additional references, we will make sure to include them in our paper.
>Since the goal is parameter estimation, it would be more convincing if the authors could conduct experiments on parameter recovery tasks and compare the discr... | Summary: The paper proposes a principled score-based method for learning jointly-specified probabilistic models in the presence of missing data in the training set. The key idea is to match the scores of marginal distributions by marginalising the missing variables first. Since marginalisation is often computationally ... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and all the errata found, we will make sure to correct them. We respond to specific questions below.
>In Assumption 4.2 you set $\theta>0$, is that a typo? I am not sure why the parameters should be positive.
Yes, sorry thank you for spotting this, it is a typ... | Summary: The paper explores score matching for missing data, proposing two distinct approaches: importance-weighted score matching and a variational approach. The importance-weighted method relies on reweighting score matching objectives using an auxiliary distribution over missing variables, while the variational meth... | Rebuttal 1:
Rebuttal: Thank you for your useful comments and feedback as well as the additional references which we will make sure to include. We address some specific points below
>However, the scope of the experiments is limited. The reliance on Gaussian data raises concerns about the generalizability of the method ... | Summary: The given paper presents novel framework for Score Matching (SM) in missing data framework. Particularly, the setting assumes that the (multi-dimensional) random variable under consideration has missing coordinates. The authors solve this problem by using an auxiliary variable that denotes the masking random v... | Rebuttal 1:
Rebuttal: Thank you for your useful comments and feedback, we address some specific points below.
>The proposed method makes sense, however, I am not sure about the evaluations. Primarily
because of the limited metrics (AUC) used to demonstrate the efficacy of proposed method.
Thanks for pointing this out... | null | null | null | null | null | null |
SAND: One-Shot Feature Selection with Additive Noise Distortion | Accept (poster) | Summary: This paper proposes SAND, a new feature selection method which modifies the original input to a linear combination of additive zero-mean Gaussian noises and the original input. The weight of linear combination $a\in \mathbb{R}^d$ measures the feature importance and the constraint of the number of selected feat... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of the paper and your valuable feedback.
Regarding being SOTA, our work emphasizes that although no single method consistently outperforms all others across every dataset, our approach is notably simpler and competitive—especially when accuracy/MAE metrics are sa... | Summary: The paper introduces SAND (Selection with Additive Noise Distortion), a novel feature selection layer for neural networks that automatically selects $k$ informative features during training. SAND operates by multiplying each input feature by a trainable gain $a_i$ while adding Gaussian noise weighted by $1-a_i... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of the paper and the valuable comments.
We agree that finding out the required number of features for a desired performance is a very valuable question to answer, but it is outside the scope of this work. By the sentence “SAND directly controls the number of sele... | Summary: This paper proposes SAND (Stochastic Additive Noise Decoupling), a method for feature selection that avoids adding any explicit loss or regularization term. Instead, it uses a simple noise-injection mechanism where each input feature is blended with noise according to a learnable gating parameter $a$. The gati... | Rebuttal 1:
Rebuttal: Thank you for the careful reading of the paper and the valuable comments.
Concerning the first question, as mentioned by the reviewer, mathematically, the optimum a_i is guaranteed to be between 0 and 1. In the code, to be on the safe side, we always clip the values between 0 and 1 at each iterat... | Summary: The key contribution of this paper is to introduce a method for feature selection during training of a neural network. A key feature of this method is to be able to do the feature selection in a very simple way, without substantive modifications to the architecture itself, while still being able to retain good... | Rebuttal 1:
Rebuttal: We appreciate your careful reading of our paper and your kind compliments. Regarding your question about learnable k, for the moment what comes to our mind is the simple idea to sweep over k and see where the loss exhibits a drastic change. | null | null | null | null | null | null |
GaussMarker: Robust Dual-Domain Watermark for Diffusion Models | Accept (poster) | Summary: This paper introduces GaussMarker, the first dual-domain watermarking technique tailored for diffusion models. The authors preoposed a
Novel Dual-Domain Watermarking taht is designed for diffusion models without requiring any fine-tuning, while still achieving strong robustness. The authors also develop GNR, w... | Rebuttal 1:
Rebuttal: **Q1:** However, in table 1, it seems that the propsoed method are more rebust than other watermarking method only under rotaion and C\&S attack. Any comments? Moreover, as the GNR is trained in latent space, does it mean that it would be reobust on image space attack like JPEG and Brightness.
**... | Summary: This paper presents **GaussMarker**, a novel watermarking method for diffusion models, with the following key contributions:
1. **Dual-Domain Watermarking**: Embeds watermarks in both the spatial and frequency domains of Gaussian noise to enhance robustness.
2. **Gaussian Noise Restorer (GNR)**: A model-indepe... | Rebuttal 1:
Rebuttal: **Q1:** Scope of Equation 10: Equation 10 is designed to improve robustness against rotation and cropping. Does it also apply to non-geometric transformations like JPEG compression and blurring? If so, can you provide a formal explanation or additional experiments? If not, can you clarify its limi... | Summary: This paper proposed **GaussMarker**, which embeds watermarks into the noise vector of diffusion models within both the spatial and frequency domains. To enhance the detection robustness of watermarks, the authors propose a learnable Gaussian Noise Restorer (GNR) that is capable of restoring from the distorted ... | Rebuttal 1:
Rebuttal: **Q1:** Why does GaussMarker largely outperform other methods when evaluated against the regeneration attack?
**A1:** This advantage stems from GNR, as shown in the SD V2.1 results presented in the table below. Regeneration using diffusion models mainly involves semantic editing, which may also i... | Summary: The paper introduces GaussMarker, a novel semantic watermark technique based on diffusion models. Different from previous works, GaussMarker adds watermarks in both the pixel and frequency domain of images. During detection, GaussMarker trains two additional components: 1. Gaussian noise restorer (GNR) for res... | Rebuttal 1:
Rebuttal: **Q1:** About "tunning-free".
**A1:** We consider GaussMarker to be a tuning-free method. The term "tuning-free" implies that SDs cannot be fine-tuned due to computational costs and the watermarking method can be attached to the model in a plug-and-play manner without touching the model weights. ... | null | null | null | null | null | null |
FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks | Accept (poster) | Summary: This paper proposes FicGCN, a framework for efficient privacy-preserving inference of Graph Convolutional Networks (GCNs) using Homomorphic Encryption, by using (1) a latency-aware packing scheme that optimally balances aggregation and combination operations based on data dimensions and model structure; (2) a ... | Rebuttal 1:
Rebuttal: **1. Security proof of FicGCN**
Thanks for your comments. FicGCN fully adopts CryptoGCN's[3] threat model and privacy assumptions which has been rigorously proved. We utilize the CKKS scheme, whose security is guaranteed by the hardness of the RLWE problem[1], ensuring polynomial-time indistingui... | Summary: The paper proposes FicGCN, a framework designed to accelerate private Graph Convolutional Network inference, with three key innovations. First, an optimal layer-wise aggregation scheduling strategy is presented to accelerate inference for data of various scales. Second, Sparse Intra-Ciphertext Aggregation (SpI... | Rebuttal 1:
Rebuttal: **1. Justification for the need to reorder nodes(Q1)**
Thanks for your comments. Negotiating and determining the optimal packing method based on NOO prior to private inference indeed constitutes a key contribution of our work. We discussed it in Section 3.2.1 and Figure 8(b).
However, only packi... | Summary: The paper presents FicGCN, a method aimed at enhancing the efficiency of homomorphic encryption in irregular Graph Convolutional Networks (GCNs). It introduces a **latency-aware packing method** that optimizes the arrangement of ciphertext slots, balancing computational overhead and utilization. The **Sparse I... | Rebuttal 1:
Rebuttal: **Time complexity of Node Order Optimization (NOO) on large-scale datasets**
Thanks for your constructive comments. We have extended the application of NOO to large-scale datasets Pokec (1.6M nodes). The following table summarizes the experimental results across datasets, including:
- **Graph St... | null | null | null | null | null | null | null | null |
TokenSwift: Lossless Acceleration of Ultra Long Sequence Generation | Accept (poster) | Summary: This work proposes a three-pronged approach (TokenSwift) to improving the latency and quality of generating ultra-long sequences: 1) Multi-token prediction similar to Medusa; 2) Using a sparse KV-cache whose elements are selected based on their attention scores (query/key inner product before softmax); 3) toke... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and insightful feedback, which has helped us improve our manuscript.
> **Q: Reformulate Claim**
**A:** We appreciate your suggestions to replace "fail" with "benefit" and will revise accordingly.
1. **Dynamic KV Cache Compression (Challenge II)**
The limitati... | Summary: As LLMs become bigger in terms of number of parameters, model inference has become computationally expensive leading need for faster and computationally efficient sequence generation. The authors propose the TOKENSWIFT, a new framework to accelerate autoregressive generation for LLMs. TOKENSWIFT utilizes multi... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable suggestions and acknowledgment of our method. We are encouraged by your recognition of ``our thorough ablation studies`` and ``the practical impact of our method, which achieves up to 3× speedup across diverse architectures``. We also appreciate your note on `... | Summary: The paper presents TOKENSWIFT, a framework to accelerate ultra-long sequence generation (up to 100K tokens) in large language models (LLMs) with lossless accuracy, addressing the time-intensive nature of such tasks (e.g., LLaMA3.1-8B taking 5 hours). It tackles three challenges—frequent model reloading, dynami... | Rebuttal 1:
Rebuttal: We sincerely thank you for your time and constructive feedback. We are encouraged by your positive assessment that ``our experiments convincingly demonstrate a 3× speedup``, ``the sensible design of our method for ultra-long sequence generation``, and ``the rigorous ablation studies on sampling me... | null | null | null | null | null | null | null | null |
An Instrumental Value for Data Production and its Application to Data Pricing | Accept (poster) | Summary: The paper analyzes how to quantify a datasets' instrumental value by taking into account the prior knowledge/data sources the buyer has. The authors argue that switching from intrinsic value (as in data Shapley) to this instrumental value is useful for avoiding overestimating the data value.
Claims And Eviden... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive comments. We will answer your questions according to logical order.
We respectfully disagree with the reviewer’s comment on relevance to the machine learning community. Below, we provide our detailed explanations:
1. In many scenarios... | Summary: The paper studies the mechanism design problem in pricing and designing data generating processes in the context of Bayesian regression. Concretely, the buyer first has a prior $q$ of the regression parameter, reports his feature $x$ that he wants prediction on, and then upon obtaining data (including the data... | Rebuttal 1:
Rebuttal: Thank you for your kind remarks and questions, and we would now like to answer your questions in logic order. Below, we first address the reviewer's major questions, and then clarify a few minor comments. We are happy to engage with any further questions.
**Re Corresponding Utility Function in ... | Summary: This paper introduces a framework for quantifying the instrumental value of data production processes (DPPs) under the bayesian linear model. The authors focus on how much additional benefit (or marginal contribution) new data brings to a decision‐maker’s task. The proposed data value is mathematically equival... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive comments. We will answer your questions according to logical order.
**Comparison with Other Data Valuation Methods**: Compared to the fairness-based Shapley value, our instrumental value accounts for sequential data settings where Shapley... | null | null | null | null | null | null | null | null |
Fishers for Free? Approximating the Fisher Information Matrix by Recycling the Squared Gradient Accumulator | Accept (spotlight poster) | Summary: - In various contexts where a method is motivated using the Fisher Information Matrix (e.g. EWC, Fisher Pruning, etc), the paper proposes to replace squared sum of gradients, which can sometimes be cumbersome to compute, by the exponential moving average of squared sums of gradients as computed in e.g. Adam, s... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback, questions, and suggested references.
We have updated our manuscript in accordance with your suggestions.
Please find our responses below.
Let us know if you have any follow-up questions, we'd be happy to discuss further.
---
> Why would a data scientist or re... | Summary: This paper introduces "Squisher," a method that repurposes the squared gradient accumulator from adaptive optimizers (such as Adam) to approximate the Fisher Information Matrix (FIM) without additional computational cost. The authors provide theoretical analysis connecting the squared gradient accumulator to t... | Rebuttal 1:
Rebuttal: Thanks for your thoughtful feedback and suggestions.
We will make sure to mention AdaFisher as adaptive method that uses a diagonal Kronecker-factored approximation of the Fisher.
Please find our responses below and let us know if you have follow-up questions.
---
> Limited insight into performa... | Summary: This paper proposes reusing the squared gradient estimator of adaptive gradient methods as an approximation to the Fisher information matrix, called the 'Squisher'. Through extensive evaluation in model merging, model pruning, sparse fine-tuning, task embedding, and continual learning, the authors demonstrate ... | Rebuttal 1:
Rebuttal: Thanks a lot for your thorough review and the various suggestions that we will make sure to incorporate into the manuscript.
We are specifically grateful to the reviewer for pointing out the equivalence between the standard and joint Fisher, which strengthens our motivation to view the standard e... | Summary: This paper explores the idea of approximating the Fisher Information Matrix by using the squared gradient accumulator that is already computed in optimizers like Adam. The authors have done an excellent job of testing this approximation in six different applications where empirical Fisher is used and show that... | Rebuttal 1:
Rebuttal: Thanks for your effort and valuable feedback.
We fixed the Adam citation in the manuscript, thanks for pointing that out.
Please find our responses below and let us know if you have any follow-up questions.
---
> [...] while you mention that squisher is better than or on par with Fisher, you sho... | null | null | null | null | null | null |
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation | Accept (poster) | Summary: This paper introduces a framework that facilitates differentiation through iLQR, which provides the gradient of an iLQR controller through implicit differentiation.
Claims And Evidence: The authors theoretically prove the effectiveness of their method in Section 5 and provide experimental results in the same ... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewers' insightful comments and constructive feedback, which have helped improve our paper. Below we provide detailed responses to each point.
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**Q1: No Theorem Proving Speedup**
We thank the reviewer for this important question. Our work is situated in the fie... | Summary: This paper proposes DiLQR, which derives the analytic gradient of a given scalar loss function with respect to the parameters in the iLQR system (e.g., parameters of the dynamics or cost functions) through the use of the implicit function theorem. Parallelization, and the sparsity of the problem are used to im... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the insightful comments and constructive feedback. Below we provide point-by-point responses to the raised questions.
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**Q1: Comparison with DiffTORI**
We thank the reviewer for raising this point. **DiffTORI** ([Wan et al., NeurIPS 2024]) is a pioneering ... | Summary: This paper introduces a differentiable iLQR controller, DiLQR, to enable scaling iLQR to longer time horizons and iteration counts. DiLQR leverages implicit differentiation at the underlying fixed-point to recover analytic gradient updates, thereby reducing computation cost in the backward pass, bypassing the ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the valuable feedback. Below we provide responses to each question.
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**Q1: Application to Higher Dimensional RL Tasks**
The field of differentiable control remains relatively young. Current real-world applications primarily focus on autonomous vehicles and... | Summary: This paper presents a method for differentiating through iLQR. Naively autodifferentiating through a trajectory optimization problem backpropagates through the iterative optimization problem, incurring a growing computational burden. Like prior works such as DiffMPC, proposing smarter ways to compute the requi... | Rebuttal 1:
Rebuttal: We sincerely appreciate the positive evaluation and constructive suggestions. Below we address the specific questions raised:
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**1. Computational Complexity Estimates (Q1):**
We clarify the computational costs with corrected exponents and iteration factors:
- **Forward pass (iLQR):**
O... | null | null | null | null | null | null |
Online Robust Reinforcement Learning Through Monte-Carlo Planning | Accept (poster) | Summary: The paper presents a robust variant of Monte Carlo Tree Search (MCTS) aimed at addressing the discrepancies between simulated and real-world environments, focusing on ambiguities in transition dynamics and reward distributions. The authors claim that their method offers a robust approach, supported by both the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive review of our paper. We will revise our paper based on these comments.
**Computation efficiency**
We want to emphasize that the robust value under all ambiguity balls with radius $\rho_T$ can be computed in at most $O(S \log(S))$ time. Thus requiring only ... | Summary: The authors address the problem of model mismatch in Monte Carlo Tree Search (MCTS)-based algorithms. They formulate their approach as a robust optimization problem under the framework of robust Markov decision processes (RMDPs) and provide both an algorithm that solves the online robust reinforcement learning... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive comments on our paper. We are encouraged by the fact that they found our paper: *provide a good overview and comparison to prior work*, our test suites are at the right level of challenge. We'd like to address several important points bel... | Summary: Reinforcement learning utilizes Monte Carlo search for planning with use of a
model. However, MC search can fail to perform well in situations where the
model does not accurately represent the transition or reward dynamics. This may
be due to inaccuracies in the model used for training an agent, non-stationari... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback.
**Baseline comparisons**
Our work focuses specifically on robust online planning using MCTS, and to the best of our knowledge, this work is the first to provide theoretical guarantees for robust online planning using MCTS, with convergence bou... | Summary: This paper presents Robust-Power-UCT, a variant of Monte Carlo Tree Search (MCTS) designed for Robust Markov Decision Processes (RMDPs). The key assumption is that exact transition and reward models are unknown, but approximate models exist with bounded uncertainty captured in an ambiguity set. This is particu... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and constructive comments on our paper. We are encouraged by the fact that they found our paper: well-written, *technically sound*, *aligns with prior work on robust RL*. We'd like to address several important points below. We will revise our paper based ... | null | null | null | null | null | null |
Learning Multi-Level Features with Matryoshka Sparse Autoencoders | Accept (poster) | Summary: This paper introduces a simple but novel approach for training SAEs with a nested structure in the feature space. As a consequence of this training, the authors present results suggesting that Matryoshka SAEs are more adept at overcoming feature splitting and feature absorption issues currently facing SAEs. Th... | Rebuttal 1:
Rebuttal: Thank you for your positive review of our paper and thoughtful feedback! We are grateful for your recognition that our work is well-written with clear language, presents relevant and extensive experiments, and effectively addresses known limitations of SAEs.
Regarding different model scales and a... | Summary: The authors suggest a novel training objective for sparse autoencoders to address the issues of feature splitting, feature absorbtion and feature composition.
They test this idea on a toy, synthetic dataset explicitly designed to demonstrate improvements, then a 4-layer transformer-based language model traied ... | Rebuttal 1:
Rebuttal: Thank you for your very positive review of our paper! We were happy to read your comment about the experimental design being excellent and your appreciation for the simplicity and novelty of the idea.
Furthermore, we appreciate your suggestion to include a few feature examples, which we will do i... | Summary: This paper presents Matryoshka SAEs, inspired by Matryoshka representation learning, that learns a nested series of SAEs simultaneously to address issues such as feature splitting and feature absorption. Comparisons with multiple well-established baseline SAEs demonstrate Matryoshka SAEs’ superior quality to o... | Rebuttal 1:
Rebuttal: Thank you for your positive review and insightful questions. We appreciate your assessment that our paper is clearly written and the idea is novel. We address your key questions below:
**Regarding the toy model demonstration:**
1. **On d < L setup:** You correctly note the toy model uses a non-... | Summary: This paper aims to improve concept learning in sparse autoencoders (SAEs), which are models that have recently become popular as a means to disentangle features from large deep models, particularly LLMs, into a sparse set of disentangled concepts. This work focuses on the problem with existing SAEs of choosing... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and valuable suggestions. We are particularly encouraged that you found the paper well-written, addressing an important problem, and believe it is a valuable contribution that should be accepted.
We address your key questions below:
1. You asked why specializ... | null | null | null | null | null | null |
A Bregman Proximal Viewpoint on Neural Operators | Accept (poster) | Summary: This paper considers the problem of efficient PDE solutions and operator learning. This paper shows that the neural operator architecture can be interpreted as the minimizer of a Bregman regularization problem, and further designs a novel architecture that includes an inverse activation function. This general ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review and the positive feedback. Should the reviewer require any further clarification, we would be delighted to provide it. | Summary: This paper proposes a novel perspective on neural operators based on Bregman proximity operators, where the action of operator layers is interpreted as the minimizer of a Bregman-regularized optimization problem. By defining the Bregman distance through Legendre functions, activation operators are characterize... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for his time and thoughtful comments and appreciate the global positive feedback. First, we would like to make it clear that the main objective of our contribution is to provide a novel theoretical framework that allows the development of founded and effective model... | Summary: In this paper, the authors proposed a new type of neural operator for solving PDE problems. The idea is to set the neural operator to be the solution of a functional optimization problem, and in particular, they choose the operator to be a Bregman Proximal operator with respect to some Legendre functions. This... | Rebuttal 1:
Rebuttal: We thank the reviewer for his positive feedback. We provide below our answers to the two questions mentioned in the review.
1. **Normally, in the context of optimization, the Bregman divergence needs to be a strongly convex function so that it represents a notion of distance. However, here it see... | null | null | null | null | null | null | null | null |
Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers | Accept (poster) | Summary: This paper examines the problem of learning multi-hop reasoning over knowledge graph facts. Prior works have shown that this is a challenging problem, particularly without chain-of-thought or externalized reasoning. However, recently, synthetic experiments have revealed that such multi-hop reasoning can be lea... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and detailed feedback, which has helped us refine both the presentation and scope of the paper. Below, we address each of the raised concerns.
**A. On theoretical presentation and unused formalisms:**
We appreciate your observation regarding th... | Summary: The paper investigates the application of grokking—a phenomenon where neural networks transition from memorization to generalization after prolonged training—to real-world multi-hop reasoning tasks. The authors propose augmenting sparse knowledge graphs (KGs) with synthetic data to increase the ratio of inferr... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed and constructive feedback.
**A. On transparency and reproducibility:**
We appreciate the emphasis on methodological clarity. In the final version, we will provide full details of the training procedure, including the exact prompts used for stru... | Summary: This paper explores extending the phenomenon of "grokking"—where neural networks transition from memorization to generalization after prolonged training—from synthetic tasks to real-world factual reasoning. The authors address the challenge of dataset sparsity in real-world knowledge graphs by proposing a data... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful and constructive feedback.
**A. On model size and generalization:**
We appreciate the suggestion regarding scaling effects. We are currently conducting additional experiments across GPT2 model sizes (124M to 1.5B parameters). Preliminary results indic... | null | null | null | null | null | null | null | null |
Code-Generated Graph Representations Using Multiple LLM Agents for Material Properties Prediction | Accept (poster) | Summary: This paper presents Rep-CodeGen, a framework using multiple LLM agents to generate, evolve, and evaluate code for obtaining graph representation of crystal structures following physical constraints. The representation obtained thereby is tested for constraint satisfaction and performance in materials property ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and thoughtful evaluation, as well as your recognition of our methodological design, theoretical foundations, and experimental framework. We are particularly grateful for your recognition of our motivation, ”Creating new representations of materials structures tha... | Summary: This paper introduces a novel framework named Rep-CodeGen, which leverages multiple Large Language Model (LLM) agents to automatically generate code for obtaining graph representations of material properties. The primary contributions of this work are threefold. First, the paper proposes an interpretable frame... | Rebuttal 1:
Rebuttal: Thank you very much for your time and for recognizing the originality of our work, the contributions to the problem, and the experimental design. Below, we will address your suggestions and questions point by point.
Q1: It is recommended to provide the initial code to help readers better understa... | Summary: This paper proposes a novel code generation framework for material property prediction. The LLM agents are employed to replace the human experts and automatically generate codes to process the CIF files fitting GNN-based models. After the processing, the obtained input vectors are called representations. Due t... | Rebuttal 1:
Rebuttal: Thank you very much to the reviewers for your time and for recognizing the innovation of our method and its contributions to the field. We will revise the paper carefully. Below, we will summarize your reviews and provide a response to each point separately.
Q1: The experiments are stopped at fiv... | Summary: This paper introduces Rep-CodeGen, a framework that uses multiple LLM agents to autonomously generate graph representations for material property prediction. Unlike traditional methods, Rep-CodeGen iteratively refines representations through crossover generation, evaluation summary, and parent selection, ensur... | Rebuttal 1:
Rebuttal: Thank you for your valuable time and your recognition of our work's motivation, novel contributions, and experimental design. We provide point-to-point responses to your questions as follows.
Q1: The neighborhood selection is the main difference between the 'new' representation and the existing m... | null | null | null | null | null | null |
Faster Rates for Private Adversarial Bandits | Accept (poster) | Summary: This work studies the adversarial bandit problem where the reward function can vary across stages and considers a differential privacy guarantee. The author proposes a novel algorithm within a batched learning framework, adding Laplace noise to the average reward in each batch to ensure differential privacy. C... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address the reviewer's main concern below and hope that they will reevaluate their score accordingly.
> Therefore, it is not entirely fair to directly compare the results with prior work, as the improvement may stem from this assumption.
While adaptiv... | Summary: The paper presents novel differentially private algorithms for adversarial bandits and bandits with expert advice. The primary contribution is an efficient conversion method that transforms any non-private bandit algorithm into a differentially private one, leading to improved regret bounds. The proposed algor... | Rebuttal 1:
Rebuttal: We thank the reviewers for their comments. We address their concerns below and hope they will reevaluate their score accordingly.
> No experiments.
We acknowledge the concern regarding the lack of experiments. However, our work is intentionally theoretical, aimed at understanding fundamental rat... | Summary: This paper presents novel differentially private (DP) adversarial bandit algorithms with improved dependency on the DP parameter $\epsilon$. It improves the regret bound from $O(\sqrt{KT\log KT}/\epsilon)$ to $O(\sqrt{KT}/\epsilon)$, ensuring no-regret even when $\epsilon \sim 1/\sqrt{T}$. Moreover, the paper'... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and noting that our work advances the frontier of privacy-preserving algorithms and could have a broader impact. We address the reviewer's main concern below and hope they reevaluate their score accordingly.
> Does Lemma 5.1 rule it out?
The reviewer is c... | Summary: This paper studies adversarial bandit problems and bandit problems with expert advice, and introduces a differentially private algorithm that achieves better regret bounds than previous approaches.
Claims And Evidence: The writing is not clear enough. See the weakness part for details.
Methods And Evaluation... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address their concerns below and hope they will reevaluate their score accordingly.
> in line 43-44, "Motivated by this gap", which gap?
By this phrase, we are referring to our comment in the previous sentence on lines 38-40: "it was not known how lar... | null | null | null | null | null | null |
CAN: Leveraging Clients As Navigators for Generative Replay in Federated Continual Learning | Accept (poster) | Summary: This paper introduces Clients as Navigators (CAN) method, which tackling catastrophic forgetting in FCL tasks due to heterogeneous client data. CAN introduces a novel Generative Replay strategy that different from existing methods by selecting teaching clients for generator training and using an adaptive data ... | Rebuttal 1:
Rebuttal: Dear Reviewer TT97:
We appreciate your engagement with our work and the thoughtful observations you made. We aim to address your concerns in our detailed responses below, hoping to provide clarity and demonstrate the effectiveness of our proposed approach.
### Response to Weaknesses
> **W1:** T... | Summary: CAN addresses the challenges imposed by non-IID data in Federated Continual Learning (FCL) and introduces a novel approach that leverages the specialized expertise of individual clients. By employing Expert-Driven Data Synthesis, CAN enhances the quality and representativeness of the generated data, ensuring e... | Rebuttal 1:
Rebuttal: Dear Reviewer z1gv:
Thank you for your encouraging remarks and the critical questions you posed. We have reflected thoroughly on your feedback and provide our detailed responses below to further clarify our method and contributions.
### Response to Weaknesses
> **W1**: The reference model Π is ... | Summary: The paper focusing on Federated Continual Learning (FCL) scenario, highlights two key observations: Client Expertise Superiority and Client Forgetting Variance. The study shifts attention from the server to the client and proposes using the unique capabilities of client-side knowledge to improve Generative Rep... | Rebuttal 1:
Rebuttal: Dear Reviewer czny:
We are truly grateful for your insightful review and the constructive feedback provided. Your suggestions helped us identify areas for clarification, and we respond to each point below with careful consideration.
### Response to Weaknesses
> **W1:** Could more experiments be... | Summary: This paper examines Federated Continual Learning, specifically exploring how client expertise aids in generating replay data and adjusting replay buffer sizes based on the forgetting variance among clients. The CAN approach first identifies accurate expert clients, utilizing their predictions to train the gene... | Rebuttal 1:
Rebuttal: Dear Reviewer aG9E:
We greatly value the time and expertise you invested in reviewing our submission. Your feedback has been instrumental in helping us improve the clarity of our work. We address your comments in detail below.
### Response to Weaknesses
> **W1:** The paper mentions that CAN is ... | null | null | null | null | null | null |
On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics | Accept (poster) | Summary: I happen to review this paper again. This paper has almost no changes when compared to previous version.
This paper studies the Clean Generalization and Robust Overfitting (CGRO) of neural networks under adversarial training. It studies the CGRO from two views: representation complexity and training dynamics... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive support and valuable feedback! We greatly appreciate the insightful review, and the recognition of highlighting the significance of our contribution and solidity of our theory, as well as the clarity of our writing. We are very glad to address the q... | Summary: This paper investigates the Clean Generalization and Robust Overfitting (CGRO) problem – defined as “robust overfitting and high clean test accuracy” (without clean overfitting/memorisation) – from perspectives of representation complexity and training dynamics. On the one hand, they show that under data assum... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback! We greatly appreciate the recognition of the novelty and significance of our contribution to the topic of adversarial robustness in the deep learning community, as well as the positive remarks on the clarity of our writing. We are very gla... | Summary: This study focuses on the phenomenon of clean generalization and adversarial overfitting. The authors theoretically formulate this phenomenon and analyze it from the perspective of representation complexity and learning dynamics. First, they derive the complexity required to learn CGRO models and robust models... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback. We are very glad to address the questions and suggestions raised by the reviewer, which we believe will help further refine our work. Below are our responses to the questions and suggestions raised by the reviewer.
**Response to claims ... | Summary: The authors explained the common Clean Generalization and Robust Overfitting (CGRO) phenomenon in adversarial training from a theoretical analysis. The authors first proved that a two-layer ReLU net will achieve CGRO with small extra parameters, and the ideal robust classifier requires exponential parameters. ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the encouraging and insightful feedback, and for highlighting the strength of our theoretical contributions and the clarity of our writing. We are very glad to address the questions and suggestions raised by the reviewer, which we believe will help further refin... | null | null | null | null | null | null |
UltraTWD: Optimizing Ultrametric Trees for Tree-Wasserstein Distance | Accept (poster) | Summary: This paper introduces an unsupervised approach to constructing an ultrametric tree for the Tree-Wasserstein Distance (TWD) that approximates the Wasserstein distance.
Claims And Evidence: - The method relies on a tree-based representation, yet the paper does not clearly define what constitutes the tree (e.g.,... | Rebuttal 1:
Rebuttal: Thank you for your valuable questions. **We will revise the paper accordingly and add more discussion on related work.**
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**Question 1.** *Could you provide a clear definition of the tree structure used in your method? Specifically, is the method limited to binary trees, or does it accommodat... | Summary: The Wasserstein distance is a well-known metric for comparing distributions, and has been used as loss function in many ML models. To improve the efficiency of computing the Wasserstein distance, researchers have considered embedding the distributions to a tree metric and computing the Wasserstein distance ove... | Rebuttal 1:
Rebuttal: We truly appreciate your positive feedback. Your valuable suggestions will help us improve the work.
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**Comment 1.** *Comparing the running time of $W_T$ with the exact computation of $W_1$ is unfair. It is not clear how many iterations does the GD algorithm require.*
**Response 1.** To ensu... | Summary: 1. Wasserstein distance has been applied to many tasks. This paper mainly focuses on how to learn an optimal tree-Wasserstein distance, which is not limited to a specific task.
2. The primary motivation of this paper is to address the suboptimal tree structures and inadequately tuned edge weights in traditiona... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review, and we sincerely appreciate your recognition of the strengths of our work, including the clear motivation, innovative algorithmic design, and strong application. We will revise the paper accordingly.
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**Comment 1.** *Is the closer your distance is to the ... | Summary: The paper proposed a method to find out an Ultra tree Wasserstein distance. The method is based on minimizing a distance between trees satisfying certain conditions for trees. Algorithm 2 proposed to find the solution under projections, meanwhile Algorithm 3 tries to reduce the computation when avoiding workin... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will modify it accordingly.
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**Comment 1.** *No guarantee for the convergence of the proposed algorithms.*
**Response 1.** Theoretical convergence is extremely hard to guarantee due to the non-convex and NP-hard nature of the problem. Prior work [1, 2] u... | null | null | null | null | null | null |
FeatSharp: Your Vision Model Features, Sharper | Accept (poster) | Summary: This paper introduces a novel upsampling method designed to address the low-resolution feature map limitations of vision encoders, particularly Vision Transformers. The proposed approach builds upon FeatUp, the current state-of-the-art upsampler, by integrating FeatUp’s Joint Bilateral Upsampling (JBU) with a ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review.
> Limited Novelty: The proposed method primarily builds upon FeatUp, incorporating well-established concepts such as tiling for handling high-resolution images and a simple learnable buffer for de-biasing. The integration of FeatUp, tiling, and attention layers... | Summary: The authors proposed a novel method for efficiently upsampling feature maps of low-resolution ViTs (CLIP) to capture fine-grained details typically lost due to limited resolution. Built upon FeatUp, their method adds de-biasing and tiles fusion modules to incorporate detailed tile features, resulting in higher... | Rebuttal 1:
Rebuttal: We thank you for your review.
> I'm curious about the performance on fine-grained image image classification datasets, like CUB-200-2011
Thank you for your suggestion. Due to time and compute constraints, we were limited to running only one further study comparing upsamplers, and we selected COC... | Summary: The paper discusses improving vision model features by refining their sharpness and resolution. It builds on the JBU algorithm to provide more detailed feature maps and studies how to clean features effectively using ViT-Denoiser's methods. The paper also enhances the AM-RADIO framework, achieving better bench... | Rebuttal 1:
Rebuttal: Thank you for your detailed review.
> what does "2x upsampling" exactly mean?
We increase the width and height by 2x.
> However, the quantitative results are not very convincing to me. For example, in Fig. 7, the neumerical results on semantic segmentation, why does 3x sampling generally under... | Summary: The paper introduces FeatSharp, a method which builds upon FeatUp (specifically its JBU upsampling variant) [1], by incorporating higher-resolution tiled views and combining them with the upsampled feature maps from FeatUp. Additionally, FeatSharp includes a de-biasing module designed to remove fixed-pattern ... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful review.
> Multi-view consistency / Fidelity Experiments: This measures the mse distance between warped-upsampled features and the encoder’s low-resolution features. While this ensures consistent alignment, it doesn’t guarantee improved semantic detail. One could a... | null | null | null | null | null | null |
LBI-FL: Low-Bit Integerized Federated Learning with Temporally Dynamic Bit-Width Allocation | Accept (poster) | Summary: The proposed method quantifies weights, activation, and gradients to lower accuracy than INT8 to significantly reduce communication and computational costs, and the proposed LBIFL can reduce communication costs by 8 times compared to full-precision FL. A large number of experiments show that compared with INT8... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and below are our detailed responses to the raised weaknesses and questions.
> **W1: Experiments on different scenarios.**
**Response:** We have made comprehensive evaluations on our LBI-FL, including image classification on CIFAR-10 using diverse archite... | Summary: The paper introduces Low-Bit Integerized Federated Learning (LBI-FL), a framework designed to reduce both communication and computational costs in Federated Learning (FL) by using quantization techniques. Unlike conventional approaches limited to INT8 precision, LBI-FL dynamically adjusts the bit-width of weig... | Rebuttal 1:
Rebuttal: Thank you for your constructive suggestions.
> **W1: Claims may raise concerns.**
**Response:** Claim i): We use this sentence to emphasize that our LBI-FL can reduce both the overhead in the uplink and downlink communication with low-bit training. In fact, the paper "Prune at the Clients, Not ... | Summary: This paper proposes a low-bit integerized federated learning (LBI-FL) framework, which reduces the uplink and downlink communication overhead and mitigates the computation burden on clients, all with a tolerable level of performance loss. Specifically, a reinforcement learning based agent, which is trained on ... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions and below are our responses to the weaknesses and questions.
> **W1: Proof of the theorem.**
**Response:** According to your comment, we have improved the theoretical analysis by relaxing Assumption 4.2 without affecting the final convergence rate. Specif... | Summary: This paper introduces LBI-FL, a novel framework for low-bit integerized federated learning with a focus on temporally dynamic bit-width allocation.
The authors provide both theoretical convergence analysis and empirical validation on multiple FL benchmarks showing reductions in communication and computationa... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and below are our detailed responses to the raised weaknesses and questions.
> **W1: Experiments on diverse tasks and datasets.**
**Response:** We have performed extensive experiments on diverse tasks and datasets to demonstrate the effectiveness of our LB... | null | null | null | null | null | null |
WILTing Trees: Interpreting the Distance Between MPNN Embeddings | Accept (poster) | Summary: This paper investigates how MPNNs learn to embed graphs in a way that captures functional relationships between them. The authors propose a novel interpretable graph distance, the WILTing distance, which effectively approximates the distances between MPNN embeddings. They demonstrate that MPNNs focus on a smal... | Rebuttal 1:
Rebuttal: Thank you for your review and valuable feedback, which help us clarify our arguments and consider future directions. Below are our answers to your questions and comments.
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> Why some MPNNs achieve better functional alignment than others
Since we focused more on trying out different datasets, ... | Summary: The authors of this paper investigate the distance function learned by message-passing neural networks (MPNNs) and introduce a new framework called Weisfeiler Leman Labeling Tree to interpret these distances. Unlike previous work that aligns MPNN embeddings with structural graph distances, the authors focus on... | Rebuttal 1:
Rebuttal: We would like to thank you for your thorough review and valuable feedback. We hope the points below adequately address your concerns.
---
> Weakness 1: How different GNN architectural choices affect the learned embedding distances and the performance of WILTing Trees. For example, how does the nu... | Summary: The paper investigates (i) the properties of the distances defined for the MPNN based on structural and functional pseudometrics to find the one that explains the high performance of MPNN, and (ii) how MPNNs learn such a structure. The main contribution is the new graph distance based on the Weisfeiler Leman l... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper, including the supplementary materials, and acknowledging our contributions. We address each weakness and question individually below.
---
> Complexity of building the tree
Suppose the dataset consists of $|D|$ graphs with at most $|E|$ edges. Then, building WIL... | null | null | null | null | null | null | null | null |
Private Lossless Multiple Release | Accept (poster) | Summary: This paper introduces a lossless multiple release mechanism for differential privacy (DP), allowing analysts with different trust levels to receive private data releases with distinct privacy guarantees. Unlike previous methods, this approach ensures that multiple releases do not accumulate unnecessary privacy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their reading and comments on our paper. We address some of the perceived weaknesses below:
**W1: only applicable to Gaussian additive noise.**
Theorem 3.5 holds for any additive noise mechanism based on a noise distribution satisfying a convolution preorder (Definition ... | Summary: This paper examines the problem of multiple private data releases with varying privacy parameters, which are assigned sequentially and in arbitrary order. Compared to [LWRR'22], the authors present a simpler analysis that avoids Brownian motion techniques and explicitly provide a sampling method. Their approac... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments on our paper. We address the perceived weaknesses below:
**W1: Relation between Algorithm 1 and the Brownian mechanism.**
We agree with the reviewer that Algorithm 1 can be interpreted as the Brownian mechanism combined with Brownian br... | Summary: This paper investigates the private lossless multiple release problem and presents a solution for a broad class of mechanisms (e.g., Gaussian, Laplacian, Poisson) based on additive noise, where the noise distribution satisfies a convolution preorder property. Unlike private gradual release, multiple release d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and questions.
Our technique applies to additive noise mechanisms as well as to invertible post-processing of additive noise mechanisms. In particular, it applies to factorization mechanisms for which the noise added to queries is correlated. This is a rathe... | Summary: This paper studies the problem of lossless multiple release under differential privacy: we want to release a noisy answers to a query satisfying various levels of privacy such that any subset of the noisy answers contain at most as much information about the statistic as the least private noisy answer.
This wo... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review and the question about the lossless multiple release setting.
Realistic privacy settings could be the military setting itself (which Bell-LaPadula was designed for), or other settings where analysts have different trust levels (here the more trusted ... | null | null | null | null | null | null |
Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic | Accept (poster) | Summary: This paper proposes a new training strategy and loss function for successful modular additions and other operations. The critical observation is the utility of sparse samples. Models learn modular addition better on sparse samples, and if sparse and dense samples are mixed, it learns from sparse samples and th... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback.
**Re: claims:** We will include revisions in the final paper to clarify the claims more generally to not assume addition. Thanks for pointing out the clarification on $0$ and $q-1$ in the modular field, we will update this in the final version.
**Re: ... | Summary: The paper improves machine learning attack baselines on LWE by training models to do modular arithmetic better. It uses custom training data and a special loss function, allowing the model to sum up to 128 elements modulo q ≤ 974269. It also shows improvements on other tasks like copy, associative recall, and ... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback.
**Re: comparisons:** In the paper, we compare our methods to the standard training approach with regular loss and default data distribution for the arithmetic, synthetic and the cryptography tasks (see Tables 3, 6, 7, and 9). We also provide a compariso... | Summary: The paper addresses the challenge machine learning models face in learning modular arithmetic, specifically in the context of the Learning with Errors (LWE) problem. It proposes two techniques: (i) using a designed data distribution that mixes sparse and dense modular arithmetic instances, and (ii) introducing... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback.
**Re: limited generalization experiments:** Per your suggestion, we conducted additional experiments with more $N$ and $q$ values. These results are presented in the response to reviewer Qesb (“additional experiments on challenging settings”).
We also ... | null | null | null | null | null | null | null | null |
Disentangling and Integrating Relational and Sensory Information in Transformer Architectures | Accept (poster) | Summary: The authors describe a neural architecture (DAT) in which relational information is a first-class object, and via a series of experiments show that this architecture offers genuine empirical benefits.
## update after rebuttal: I have kept my "accept" score for this strong paper. There is no change since my re... | Rebuttal 1:
Rebuttal: Thank you for your detailed and thoughtful review. We appreciate your positive feedback about the significance, methodological soundness, and strength of the empirical evaluation. Below, we hope to address the main concerns you raised in turn.
**D1: Interpretability of Learned Relations**
> Figu... | Summary: This paper presents the Dual Attention Transformer (DAT), an extension of the Transformer architecture that introduces a relational attention mechanism alongside the standard self-attention mechanism. The key idea is to explicitly represent and process relational information by replacing the standard value agg... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and positive feedback on the overall contribution of our work, especially for highlighting the empirical support for our claims, the contribution to relational reasoning in Transformer architectures, and the importance of structured reasoning in deep learning m... | Summary: The authors propose a parameter-efficient variant of the self-attention mechanism in transformers called the Dual-Attention Transformer (DAT), which explicitly routes both sensory information (about individual tokens) and relational information (about relationships between pairs of tokens). The key differences... | Rebuttal 1:
Rebuttal: # Response
Thank you for your detailed and thoughtful review. We appreciate your positive assessment of our work and are encouraged by your recognition of its methodological soundness, strong empirical results, and relevance to the literature on relational inductive biases and Transformer-based a... | Summary: The authors introduce a modification/extension of the classical attention mechanism they term ‘dual attention’, which not only routes sensory information (as in classic SA) but adds a dedicated pathway to exchange relational information between tokens using its unique attention matrix – allowing for informatio... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We appreciate your positive feedback on the originality, significance, and clarity of our work. We are especially grateful for the time and effort you took to thoroughly engage with our work, reading the appendix and making note of typos. We'd... | null | null | null | null | null | null |
Directed Graph Grammars for Sequence-based Learning | Accept (poster) | Summary: In this paper, the authors propose a novel framework for mapping Directed Acyclic Graphs (DAGs) to sequences via directed graph context-free grammars. Importantly, the authors formulate graph grammar induction as a Minimum Description Length (MDL)-based compression, achieved through an intricate sequence of gr... | Rebuttal 1:
Rebuttal: Thank you for acknowledging the novelty and strengths of our interesting, theoretically principled framework!
*There are some clear scalability concerns due to NP-hard grammar induction, and the authors propose a brute-force approach…. There is a list of possible adjustments and heuristics in the... | Summary: This paper discusses how to convert a directed acyclic graph (DAG) into a sequence, allowing for sequence decoding based on autoregressive models. The paper proposes a method to transform a graph into a sequence in the form of a context-free grammar. The core idea is to induce the grammar from existing data us... | Rebuttal 1:
Rebuttal: *Thank you for recognizing the reasonableness and positive experimental results of our work!*
* In addition to recognizing our method as “converting a graph into a sequence”, we want to add there is a deep motivation from the objectives of **compositional generalization**! Contrary to what a “seq... | Summary: This paper describes a graph grammar approach for mapping graphs to strings in a principled way.
Given a set of graphs, the underlying grammar induction is deterministic and determines grammar production rules able to reconstruct an in principle unbounded graph ensemble, containing the "training" graph set.
C... | Rebuttal 1:
Rebuttal: Thank you for recognizing our paper as well-written and the attention to detail in your review!
*... combination with transformers… and thus a weakness of the paper is not spending some more time on this…. what do you think can be hurdles that this would face?*
* To clarify: we **did** use Trans... | Summary: This paper proposes representing directed acyclic graphs as sequences of production rules. These sequence-based representations enable generative modeling using language-like models, such as transformers. The authors train and evaluate these models in various scenarios, including neural architecture search, Ba... | Rebuttal 1:
Rebuttal: Thank you for recognizing our strong experimental results and asking insightful questions!
*The proposed representations appear quite complex… a more detailed rationale for why simpler alternatives (e.g., positional encoding) are insufficient would have been valuable…. the authors should compare ... | null | null | null | null | null | null |
Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression | Accept (poster) | Summary: This paper investigate the importance of early stopping in well-specified high-dimensional logistic regression. They demonstrate that the early stopping ensures generalization in terms of excess logistic risk, while interpolator diverges. These results emphasize the need of early stopping in overparameterized ... | Rebuttal 1:
Rebuttal: Thank you for supporting our paper. We answer your questions as follows.
---
**Q1.** “The early-stopping time is oracle-based and lacks explicit bounds, which will limit the practical utility of the results.”
**A1.** Our aim is to understand the benefits of early stopping. We believe designing a... | Summary: The paper studies the impact of early stopping in gradient descent (GD) for overparameterized logistic regression. It demonstrates that, in this setting, early-stopped GD is well-calibrated and achieves lower risk and zero-one risk compared to GD at convergence. Additionally, the paper establishes a connection... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. We address your questions below.
---
**Q1.** “The technique used to derive the logistic risk upper bounds relies heavily on the prior work of Telgarsky (2022).”
**A1.** While our Lemma 3.3 appears in [Telgarsky 2022] (and other prior works as mentioned)... | Summary: This paper studies the problem of early stopping in logistic regression. It considers two metrics: the zero classification accuracy and the logistic loss. The paper shows that the excess zero one loss is bounded by the excess logistic loss. Building on this the paper shows that there exists an early stopped mo... | Rebuttal 1:
Rebuttal: Thank you for your comments. We address your concerns as follows.
---
**Q1.** “My current score is primarily due to the exists of $t$ such that $\\hat L(w\_t) \\le \\hat L(w\_\{0:k\}\^\*)$. I believe that the existence of such a $t$ needs to be proven to complete the story.”
**A1.** The existenc... | Summary: This paper theoretically examines the additional regularization bias introduced by early stopping in logistic regression. The authors first demonstrate that for any well-specified logistic regression problem, gradient descent (GD) with oracle-based early stopping is well-calibrated and statistically consistent... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments. We address your questions below.
---
**Q1.** “...Lines 266-273, discuss application of these bounds to a trivial, under-parameterized case. How do these bounds perform in the overparameterized case?... For a meaningful comparison with the negative results in ... | Summary: The authors examined high-dimensional logistic regression scenarios where p could be finite or infinite. They analyzed the gradient flow dynamics of logistic regression, discussed the generalization capabilities of early stopping estimators and interpolators, and provided comparisons between the gradient desce... | Rebuttal 1:
Rebuttal: We appreciate your support! We address your concerns as follows.
---
**Q1.** “Though the settings are different, I am not sure if the authors ignored the line of work on logistic regression by E. Candes et al.”
**A1.** We will cite and discuss these works you pointed out in the revision. They... | null | null | null | null |
SING: Spatial Context in Large Language Model for Next-Gen Wearables | Accept (poster) | Summary: This paper proposes a method for aligning spatial audio sensing to LLM embeddings. The approach includes CNN-based DoA estimation module and automatic speech recognition module from OpenAI. The paper introduces an OmniTalk dataset that is synthetically generated to train the module. The experiment evaluated th... | Rebuttal 1:
Rebuttal: **1: Why we need a spatial positioning instead of speaker diarization.**
Thank you for the question. While speaker diarization answers "who spoke when," it lacks spatial awareness—crucial for many applications. Our focus on Direction of Arrival (DoA) estimation adds spatial context to speech, ena... | Summary: The paper leverages the Owlet monaural microphone, with superior direction-of-arrival sensing (DoA), to endow LLMs with spatial audio awareness towards more intelligent wearables and other usability scenarios. To achieve this, the authors prepare synthetic variants of the LibriSpeech dataset with ground-truth... | Rebuttal 1:
Rebuttal: **Comment 1: 0-speaker case should be considered.**
Thank you for pointing this out. In the current version of Table 2 and Figure 11, we focus on scenarios with one or more speakers. However, we agree that it would be valuable to explicitly consider the 0-speaker case. To address the 0-speaker ca... | Summary: In this paper, the authors introduce SING, a system that integrates spatial speech understanding into LLMs to enhance context-aware applications for wearable devices. SING uses microstructure-based sensing with a monaural microphone to extract Direction of Arrival information and combines it with linguistic e... | Rebuttal 1:
Rebuttal: **Comment 1: I suggest the authors to introduce or define what “spatial” or “spatial feature” is in the early of the Introduction section.**
We appreciate the reviewer’s insightful comment. We agree that the term “spatial” can be interpreted in multiple ways depending on the context. To clarify o... | Summary: This paper introduces SING, a system that integrates spatial speech understanding into LLMs for wearable applications. It leverages microstructure-based spatial sensing to extract Direction of Arrival (DoA) information using a single monaural microphone. Spatial cues are fused with Whisper embeddings and align... | Rebuttal 1:
Rebuttal: **1: Lacks real-world dataset validation. Results are based on synthetic data.**
We used real-world impulse responses measured from calibration of the acoustic frontend and followed standard practices from acoustic physics principles. This methodology enables creation of a diverse, controlled dat... | null | null | null | null | null | null |
Cooperation of Experts: Fusing Heterogeneous Information with Large Margin | Accept (poster) | Summary: The authors propose the Cooperation of Experts (CoE) framework to fuse heterogeneous information in multiplex networks. Authors design a two-level expert system where low-level experts focus on individual network layers while high-level experts capture cross-network relationships. The framework employs a large... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the encouraging and constructive feedback. We greatly appreciate your recognition of our motivation, expert coordination design, and robustness to structural perturbations. Below we provide detailed responses to your suggestions. Figures and Tables are summarize... | Summary: This paper proposes the Cooperation of Experts (CoE) framework, which solves the problem of multimodal heterogeneous information fusion by constructing a heterogeneous multiplexing network. The research focuses on the challenge of pattern heterogeneity across semantic spaces, designing specialized encoders as ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. Your constructive criticism is invaluable in refining our work. Below, we give point-by-point responses to your comments.
**Weakness 1**
We fully agree that deeper theoretical analysis strengthens the credibility of a new learning framework. To clarify, w... | Summary: This paper proposes the Cooperation of Experts (CoE) framework, which aims to address the challenge of fusing heterogeneous information in modern data analysis. The CoE framework encodes multi-typed information into unified heterogeneous multiplex networks and allows dedicated encoders, or "experts," to collab... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. Your constructive criticism is invaluable in refining our work. Below, we give point-by-point responses to your comments. Figures and Tables are summarized in this link: **https://anonymous.4open.science/r/ICML_rebuttal-7D0E**.
**Weakness 1 & Question 1: Mo... | Summary: This paper presents the CoE framework, a groundbreaking method for extracting knowledge from diverse and multi-layered networks. Its core novelty lies in a hierarchical expert coordination mechanism, where specialized low-level experts focus on capturing unique relational patterns, while high-level experts int... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful feedback. We are especially grateful for the positive recognition of our novel expert coordination strategy, solid theoretical foundation, and strong empirical performance. Below, we provide responses to the specific suggestions and questions. Figures and Tables are s... | null | null | null | null | null | null |
Tensor Decomposition Based Memory-Efficient Incremental Learning | Accept (poster) | Summary: This paper addresses the challenge of memory efficiency in Class-Incremental Learning (CIL), whose goal is continuously learning new classes over time. Replay-based methods, a prominent approach in CIL, suffer from high memory consumption due to the need to store past exemplars. To mitigate this, the authors p... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's insightful feedback and constructive suggestions. Below, we address the key concerns raised.
### **1. Why diversity can be ensured by the second-stage sampling and its contribution to performance**
The paper proposes a novel exemplar selection strategy that... | Summary: This paper presents a new memory-efficient method for CIL. Different from previous papers, tensor decomposition is used to compress original image. Besides, a new exemplar selection strategy is proposed to ignore the influence of negative compressed samples. Extensive experiments on different datasets demonstr... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and constructive suggestions. Below, we address the concerns raised.
### **1. Lacks ablation experiments for the proposed method**
In our manuscript, we have conducted experiments (Section 4.2, Fig. 3, and Tab. 6) to evaluate the impact... | Summary: The paper addresses the challenge of catastrophic forgetting in Class-Incremental Learning (CIL), where models struggle to retain previous knowledge when incrementally learning new classes. While replay-based methods mitigate this by storing old exemplars, their high memory consumption limits practicality. Exi... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's constructive feedback and valuable suggestions. Below, we address the concerns raised.
### **1. Failing to explicitly report the actual memory costs**
In our manuscript, we have provided parameter configurations of different datasets in Tab. 7, e.g., for CIFAR, $R = ... | Summary: This paper applied tensor decomposition on the replay-based continual learning methods. To minimize the influence of the reconstruction error on the training, the reconstructed images with low reconstruction error are selected for storage. The method is validated combined with other replay-based methods.
Clai... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s thoughtful evaluation and constructive feedback. Below, we address each concern raised and outline revisions to strengthen the manuscript.
### **1. Additional experiments are needed to validate the importance of incorporating reconstructed samples into train... | Summary: This paper introduces a novel approach to Class-Incremental Learning (CIL) that addresses memory efficiency challenges in replay-based methods. By employing tensor decomposition techniques instead of traditional pixel-level compression, the method exploits the low intrinsic dimensionality and pixel correlation... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback and constructive suggestions. Below, we provide a point-by-point response to the comments raised.
### **1. Inconsistent terminology for evaluation metrics**
In the revised manuscript, we will align our metric naming with the community s... | null | null | null | null |
Learning to Match Unpaired Data with Minimum Entropy Coupling | Accept (poster) | Summary: This paper proposes a novel method to solve the continuous Minimum Entropy Coupling (MEC) problem. Specifically, it incorporates generative diffusion models to learn the joint distribution with the minimum joint entropy, while enforcing a relaxed version of the marginal constraints.
Claims And Evidence: In ge... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful feedback and suggestions. Below, we provide responses and additional experiments to address their concern.
> 1. Could you add one dataset to each application?
- **Additional Image translation experiment**:
We consider the CelebA-HQ dataset (Karras et al... | Summary: This paper proposes minimum entropy coupling (MEC) to align unpaired multimodal data. MEC seeks a joint distribution with the desired marginals that is optimal in the sense of minimum entropy, in comparison to optimal transport approaches which minimize an integrated cost. This sidesteps the difficulty of spec... | Rebuttal 1:
Rebuttal: Thank you for recognizing the relevance of the MEC framework in coupling unpaired data and for the insightful feedback, which we address next.
> Another single-cell dataset.
We perform new experiments using the peripheral blood mononuclear cells (PBMC) dataset, as the CITE-seq dataset is used i... | Summary: The manuscript presents a novel method for matching unpaired data through Minimum Entropy Coupling (MEC). By extending MEC to continuous distributions and leveraging denoising diffusion probabilistic models (DDPMs), the authors propose a cooperative framework that alternates between two conditional generative ... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing the contribution of our method for matching unpaired data and the insightful feedback which we address in the following. As suggested by the other reviewers as well, we performed additional experimental campaigns to challenge our DDMEC method.
> 1- Could you ... | null | null | null | null | null | null | null | null |
Rhomboid Tiling for Geometric Graph Deep Learning | Accept (poster) | Summary: This paper proposes Rhomboid Tiling (RT) clustering, a hierarchical clustering method designed for geometric graph deep learning. The method leverages higher-order Voronoi structures to improve graph pooling and demonstrates competitive performance across multiple benchmark datasets.
Claims And Evidence: The ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful and constructive feedback. Below we address each concern raised.
**1. Dataset Description**
To address the reviewer’s concern, we provide a summary of the datasets used in our classification experiments:
**Table: Description of classification d... | Summary: This paper proposes a geometry-aware graph clustering algorithm for enhancing geometric graph classification performance across diverse datasets. The method captures high-order structural information of geometric graphs through high-order Voronoi tessellation and Delaunay complexes. Building on this foundation... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive recognition of our rhomboid tiling clustering framework and its potential for broader applications.
**1.Question about geometric conflicts**
Thank you for the insightful question.
Our method adopts a **layer-wise hierarchical clustering** strategy. Given a ... | Summary: This paper introduces Rhomboid Tiling (RT) clustering, a novel hierarchical clustering method for geometric graph deep learning. The RT clustering approach is based on the rhomboid tiling structure, which extends Voronoi tessellation and Delaunay complex theory to efficiently capture high-order geometric relat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable and constructive feedback. Below we respond to the key concerns raised.
**1. Readability**
We have revised our notation and mathematical expressions to improve consistency and clarity—e.g., clarifying the usage of matrices $C_l$ and $C_k$. Additionally, w... | Summary: The author proposes a new pooling methgod inspired by the the rhomboid tiling structure. The author starts from “High-Order Voronoi Tessellation”, which is a method to partitate space based on points. The Voronoi cell Q indicates that all points blong to Q are clustered together and separated with other points... | Rebuttal 1:
Rebuttal: We thank the reviewer for their helpful feedback. Below, we provide detailed responses to each of the points raised.
**1. Model Efficiency**
To address the reviewer’s concern, we provide both the **theoretical time complexity** of RTPool and **empirical evidence** comparing it with other pooling... | Summary: The paper introduces a novel hierarchical clustering method—Rhomboid Tiling (RT) clustering—for geometric graph deep learning. Unlike traditional clustering-based pooling methods that mainly rely on graph connectivity, RT clustering leverages high-order geometric structures derived from concepts such as alpha ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and constructive comments. Below, we provide detailed responses to each of the points raised.
**1. Hyperparameter sensitivity**
To address this concern, we have conducted a detailed sensitivity analysis by varying the value of $k_2-k_1$. The r... | null | null | null | null |
WOMD-Reasoning: A Large-Scale Dataset for Interaction Reasoning in Driving | Accept (poster) | Summary: The authors provided the new Q&A Reasoning dataset built on top of the famous Waymo Open Motion Dataset (WOMD-Reasoning) with the help of ChatGPT-4 and MetaDrive simulator for visualization purposes, and checked the baseline performance of Motion-LLaVA on top of it.
## update after rebuttal
Authors did a slig... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful and constructive feedback. We address your comments and concerns below.
> Q1. Human Assessment Scale.
We are grateful for the reviewer's suggestion on providing stronger evidence of validations. To explore the validity of our assessment, we further enlarge... | Summary: The paper introduces WOMD-Reasoning, a large-scale, multi-modal dataset for reasoning about interactions in autonomous driving, focusing on traffic rule-induced and human intention-induced interactions—areas underrepresented in existing datasets. It also presents Motion-LLaVA, a multi-modal model fine-tuned on... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's insightful feedback. Please see our response below.
> Q1. Details on the BEV input used for LLaVA & Evaluations on real-world scenarios.
We appreciate the reviewer's suggestions:
1) **For the BEV input**, it comes from plotting WOMD data. Road elements are plotted wi... | Summary: The paper introduces WOMD-Reasoning, a large-scale dataset designed for interaction reasoning in autonomous driving, built upon WOMD. The dataset addresses a critical gap in understanding traffic rule-induced and human intention-induced interactions, which are often overlooked in existing driving datasets that... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and insightful suggestions. Please see our responses below:
> Q1. Dataset accuracy: Eval criteria, diversity and annotation reliability.
We thank the reviewer for questions on validations, please see our responses below:
1) **Evaluation Criteria**:... | Summary: This paper introduces the Waymo Open Motion Dataset-Reasoning (WOMD-Reasoning), a comprehensive question-and-answer dataset designed to articulate and assess the interactions prompted by traffic rules within driving scenarios. To demonstrate the utility of WOMD-Reasoning, the paper proposes Motion-LLaVA, a mot... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive comments and insightful suggestions, and we address your comments and concerns below.
> Q1. More LM baselines.
We are grateful for the suggestion. While our main goal for fine-tuning Motion-LLaVA is to show the effectiveness of WOMD-Reasoning, we agree that... | null | null | null | null | null | null |
DictPFL: Efficient and Private Federated Learning on Encrypted Gradients | Reject | Summary: This paper studies the problem of using homomorphic encryption in federated learning. The idea is to use Lookup-based Convolutional Neural Network (LCNN), and only encrypt a small fraction of model weights. Furthermore, positions with small scale of gradients are pruned from uploading. Experiments are conducte... | Rebuttal 1:
Rebuttal: We thank Reviewer C4sK for providing constructive comments.
**Q1. Why is the gradients privacy not compromised? Pruning may let the server know the distribution of large entries in the gradients. Please clarify what information is protected under the semi-honest setting.**
The gradient's privacy... | Summary: The paper introduces DictPFL, a novel framework for privacy-preserving federated learning that fully encrypts shared gradients while maintaining efficiency.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: The paper presents theoretical claims regarding the relationship betw... | Rebuttal 1:
Rebuttal: We thank Reviewer Y4dC for the thorough reading of our manuscript and for providing constructive comments.
**Q1. Misleading "full encryption": Dictionary D and pruning patterns may leak privacy.**
Our "full encryption" refers explicitly to encrypting all information that clients share with the ... | Summary: The paper proposes DictPFL, a novel framework for federated learning that addresses the trade-off between privacy and efficiency in homomorphic encryption (HE)-based FL. By decomposing model weights into a fixed dictionary and a trainable lookup table (DePE) and further pruning gradients via encryption-aware p... | Rebuttal 1:
Rebuttal: We thank Reviewer jD6X for the thorough reading of our manuscript and for providing constructive comments.
**Q1. From Section 4.1, DePE is established on the assumption that a public model is shared at the initial of FL, which may not always hold.**
Please refer to the Reviewer C4sK's Q2.
**Q2... | Summary: This paper proposes a strategy that selectively encrypts only important weights using Dictionary-based Pruning and Holistic Reactivation Correction (HRC) techniques. This approach maintains the strong security of homomorphic encryption while reducing communication costs and improving training speed. Experiment... | Rebuttal 1:
Rebuttal: We thank Reviewer 4p9J for the thorough reading of our manuscript and for providing constructive comments.
**Q1. Security evaluation is limited to Gradient Inversion Attacks, and the paper does not assess its resistance to other security threats. Do you plan to conduct additional security evaluat... | null | null | null | null | null | null |
Branches: Efficiently Seeking Optimal Sparse Decision Trees via AO* | Accept (poster) | Summary: The paper introduces BRANCHES, a search method for decision trees. Using an AND/OT graph formulation for the decision tree search, the method relies an OA*-like exploration strategy with purification bounds for the heuristic.
The introduced method provably recovers the optimal decision tree and the efficiency ... | Rebuttal 1:
Rebuttal: Thank you for very much for your reviews, please find our response below:
## Missing literature
- Chaouki et al. (2024)'s TSDT algorithm is tailored to online classification where a data stream is observed instead of a batch of data, which is different from the batch setting we consider. Due to t... | Summary: The paper presents Branches, a new approach for computing optimal decision trees by formulating the problem as AND/OR graph search and proposing an AO*-type algorithm that solves the problem. The proposed approach learns non-binary trees (i.e., trees with multiway splits) for non-binary features. The author pr... | Rebuttal 1:
Rebuttal: Thank you very much for your review, please find below our response:
## Interpretability of multi-way splits
- Thank you for raising this point. The interpretability of DTs is due to their simple decision rules, it is not specific to binary DTs. On the other hand, we recognise that DTs where each... | Summary: The paper considers the problem of learning an optimal decision tree for a given dataset. Specifically, the DT learning problem is formulated as a heuristic search problem over an AND/OR graph representing the space of all possible DTs. Consequently, an efficient best-first search algorithm (aka AO* search) ca... | Rebuttal 1:
Rebuttal: Thank you very much for your reviews, please find our response below:
## Presentation:
Thank you for this feedback. We have included Figure 2 in the Appendix for the purpose of illustrating the notions of branches and sub-DTs. Do you have any recommendation that would improve this figure's qualit... | Summary: This paper presents "BRANCHES," a novel algorithm for learning optimal decision trees (DTs) by integrating Dynamic Programming (DP) and Branch & Bound (B&B) techniques. The study addresses the trade-offs in existing methods—where some approaches provide efficient DP strategies but lack strong pruning bounds, a... | Rebuttal 1:
Rebuttal: Thank you very much for your reviews, please find below our response.
## Missing Literature:
- (McTavish et al. 2022): We cite these authors, in fact we use their implementation of GOSDT as mentioned in Appendix H. We also directly quote them in Appendix H.4. However, we only compared with GOSDT ... | null | null | null | null | null | null |
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