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Zero-Shot Learning of Causal Models | Reject | Summary: Learning the causal generative process from observational data is a challenging problem bottlenecked by the necessity of learning a separate causal model for each dataset. This paper studies a unifying framework to enable zero-shot inference of causal generative processes of arbitrary datasets by training a si... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! We appreciate your recognition of the soundness of our framework and the diversity of our experiments. We now address the concerns raised by the reviewer below.
> Access to noise samples
Thank you for raising this point. We agree... | Summary: This paper introduces a method called Cond-FiP for transfer learning of causal mechanisms in causal systems, specifically Structural Causal Models (SCMs). Given the causal variables and their graph, the approach aims to learn a single model capable of inferring the distributions of causal variables without dat... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful feedback! We appreciate that they found our claim regarding Cond-FiP’s performance relative to state-of-the-art methods well justified. Additionally, we highlight our experiments in scarce data regimes (Appendix E), where Cond-FiP demonstrates superior ge... | Summary: This paper addresses the problem of inferring structural causal models (SCMs) from observational data. Unlike previous approaches that train separate models for each observational dataset, this work proposes learning a single model across a distribution of problem instances, enabling zero-shot inference of the... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive and insightful feedback! Thank you for acknowledging the soundness of our framework and diverse experiments. We now address the concerns raised by the reviewer below.
> The paper claims that the proposed method performs well in low-data scenarios. Table ... | null | null | null | null | null | null | null | null |
Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss | Accept (spotlight poster) | Summary: Lipschitz neural networks are either trained or constructed such that their Lipschitz constant is small, enabling easy verification of the network to adversarial perturbations. Ways of obtaining networks with small Lipschitz constants include a) regularising the network at training time or b) designing layers ... | Rebuttal 1:
Rebuttal: **Q: Regarding performance on large perturbations**
BRONet indeed achieves the best performance on both clean and certified accuracy at $\varepsilon = 36/255$, but is less consistent for larger perturbations $\varepsilon$. Interestingly, we have observed that less expressive Lipschitz models tend... | Summary: Lipschitz neural networks allow certified robustness without inference overhead; they are built by composing constrained layers. In this paper, the authors propose two improvements over the previous state of the art: they introduce a novel parametrization to construct orthogonal convolutions (BRO convolution),... | Rebuttal 1:
Rebuttal: **Q: Regarding Standardization and Certification**
For the BRONet/LiResNet experiments, the dataloader outputs data in the [0,1] range without any additional standardization or normalization, as could be confirmed by the dataset functions in the `bronet/tools/dataset/` folder.
For the LipConvNet... | Summary: This paper introduces a new 1-Lipshitz layer using the Block Reflector Orthogonal (BRO) parameterization of low-rank orthogonal matrices for constructing image classifiers with certified robustness. In addition a new logit annealing loss function is developed to balance margin learning across data points, addr... | Rebuttal 1:
Rebuttal: **Q: Regarding Theorem 1 and Proposition 3**
Thank you for your feedback. We will enhance the clarity of Theorem 1 by presenting its proof in a dedicated subsection. For Proposition 3, it is from (Ledoux and Talagrand 2013) and we will explicitly cite it in the statement for clarity.
---
**Q: R... | Summary: The paper proposes a new method to construct 1-Lipschitz neural networks, namely, the L2 norm Lipschitz constant for each layer is 1. A 1-Lipschitz network is very useful for guaranteeing the robustness of neural networks. The paper claims to outperform existing 1-Lipschitz network designs such as SOC and Cayl... | Rebuttal 1:
Rebuttal: **Q: Regarding Table 1 Description**
Thank you for your feedback. Table 1 indeed presents the combined results with the LA loss, as stated in the Appendix. To ensure clarity, we will explicitly indicate this by adding a (+LA) notation in the revised version.
---
**Q: Fair Comparison of Differen... | null | null | null | null | null | null |
Selective Preference Aggregation | Accept (poster) | Summary: This paper proposes aggregating ordinal preferences by producing selective rankings. The proposed selective aggregation framework explicitly reveals and controls dissent. The authors develop efficient graph-based algorithms (Algorithm 1 and Algorithm 2) with theoretical guarantees on correctness, uniqueness, a... | Rebuttal 1:
Rebuttal: Response
Thank you for your feedback! We address them and include tables at https://tinyurl.com/2ybsfs95
> Is the focus on the algorithmic framework for selective aggregation, or on its application to a specific problem domain?
The primary contribution is the proposed algorithmic framework. We ... | Summary: The paper introduces a new preference aggregation solution, called Selective Preference Aggregation (SPA). Its essential feature is to return a partial order of items based on beyond-majority principles. More precisely, for any $\tau \in [0, 0.5)$, SPA constructs a total order over the finest partition of the ... | Rebuttal 1:
Rebuttal: Thank you for your time and feedback! We appreciate your feedback and your detailed suggestions for improvement, including further datasets to improve our work. We provide tables at https://tinyurl.com/2ybsfs95
> However, the experiment in Section 6 is less compelling … The reported prediction e... | Summary: The paper introduces a new framework for ranking via preference aggregation while allowing for disagreement of the voters. Unlike many traditional methods that enforce a total order, the approach aims to construct a partial ranking, only comparing items where a sufficient majority agrees. The paper proposes an... | Rebuttal 1:
Rebuttal: Response
Thank you for your time and feedback! We include supplementary tables here: https://tinyurl.com/2ybsfs95
> The authors claim that the algorithm is fast and scalabile however this is not convincing …
> … 60-100 seconds for 500 items seems rather slow.
As you’ve noted, our empirical runti... | Summary: This paper introduces selective preference aggregation (SPA), a framework that aggregates ordinal preferences into partial orders (tiered rankings) to avoid arbitrating disagreements. The core contributions include a graph-based algorithm, theoretical guarantees (e.g., stability under missing data), and empiri... | Rebuttal 1:
Rebuttal: We thank you for your response! We provide tables at https://tinyurl.com/2ybsfs95
> In RLHF settings where the number of items n is typically small (<5) …This would clarify whether SPA’s advantages (e.g., robustness, transparency) persist in the small-scale preference comparisons characteristic o... | null | null | null | null | null | null |
Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision | Accept (poster) | Summary: The paper introduces a novel method to improve the performance of pruned large language models (LLMs) by combining sparsity with a low-rank approximation. The authors propose an iterative refinement algorithm that updates the sparse weight matrix while incorporating a low-rank component to approximate the diff... | Rebuttal 1:
Rebuttal: We authors greatly thank the reviewer for constructive comments on this work. We would like to clarify the following points:
**W1: Evaluation Scope: Lacks zero-shot and few-shot task evaluations (cf. Wanda’s Tables 2, 21), limiting practical relevance.**
**Q3: Zero-shot Task Evaluation: Why wer... | Summary: In this work, the authors proposes a low-Rank refinement method to factorize a dense full matrix into a sparse matrix and a low-rank matrix, bridging the performance gap between dense and sparse models. This approach iteratively improves the sparse weight matrix through a low-rank adjustment, thereby increasin... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our paper. We appreciate your constructive feedback and suggestions.
**W1 (Computational Complexity Analysis): While the paper discusses parameter efficiency of low-rank refinement, it does not thoroughly analyze the computational cost of the iterat... | Summary: This paper introduces a novel approach to improve the performance of sparse language models through low-rank refinement. The main contribution of the paper is a method that refines sparse models using a low-rank refinement, which leads to improved precision. This approach is theoretically grounded, with proofs... | Rebuttal 1:
Rebuttal: **W1: While the paper mentions magnitude pruning and N:M structured pruning, it does not discuss structured sparsity techniques, such as block sparsity or channel pruning, which have been shown to improve hardware efficiency and model performance.**
We thank the reviewer for pointing out the impo... | Summary: Magnitude pruning removes weights that have the smallest absolute values. However, traditional pruning methods require re-training the model to recover performance, which is computationally expensive and requires extensive data or teacher model. To address this, the authors propose to address this by approxima... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback and constructive suggestions.
**W1: End-to-end inference acceleration is missing. It's better to report speedup for completeness.**
1. Without sparse matrix formats or specialized hardware acceleration, the inference time is as follows:
Firstly, for a fair ... | null | null | null | null | null | null |
Stable Offline Value Function Learning with Bisimulation-based Representations | Accept (poster) | Summary: The paper tackles the field of offline policy evaluation (OPE) and addresses methodology to find good state-action-pair representations. It introduces a kernel based state-action representation and gives theoretical properties for it. It then presents experimental results of the introduced KROPE method on diff... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful comments and feedback. Thank you for acknowledging that the work was an interesting read with valuable contents.
Your comments are helpful in making our paper precise. We do believe, however, that these adjustments involve minor reframings/edits. We address... | Summary: This paper introduces Kernel Representations for Offline Policy Evaluation (KROPE), a kernel-based representation learning algorithm based on bisimulation metric-like ideas. They study a class of representations which emerge as the solution to the representation learning loss, and prove that it has desirable t... | Rebuttal 1:
Rebuttal: Thank you very much for appreciating our work and the clarity of our writing. Thank you for mentioning the strengths of our empirical and theoretical work, especially Theorem 1 and our choice of experiments. We also appreciate your acknowledging the significance of our results and potential for fu... | Summary: The paper introduces Kernel Representations for Offline Policy Evaluation (KROPE), a novel algorithm designed to stabilize offline value function learning in reinforcement learning. KROPE leverages π-bisimulation to shape state-action representations, ensuring that similar state-action pairs are represented co... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging that our empirical results and theoretical results are rigorous, and that the paper is clear and well-organized.
Below we address your concerns.
**Re: reason to learn the value network (“the paper does not further elaborate on the advantages or potential a... | Summary: This paper addresses offline policy evaluation in offline RL, which involves estimating expected returns of state-action pairs under a fixed policy using offline datasets. Stability in this estimation process is critical for accurate evaluation. The authors propose KROPE, a new method combining bisimulation-ba... | Rebuttal 1:
Rebuttal: Thank you for acknowledging that our empirical results and theoretical results are solid. Below we address the concerns you raise.
**Use of Mean Absolute Error vs. MSE**
This suggestion is valid since we understand the MAE may be more robust to outliers. However, it does not diminish the validit... | null | null | null | null | null | null |
ZipAR: Parallel Autoregressive Image Generation through Spatial Locality | Accept (poster) | Summary: The paper proposes ZipAR, a training-free plug-and-play decoding method for accelerating auto-regressive visual generation models. It decodes spatially adjacent tokens in the column dimension in parallel. It emplys an adaptive local window assignment scheme with reject sampling strategy. Experimental results d... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Utilize additional metrics to fully evaluate the method.**
To address this concern, we have expanded our evaluation by assessing ZipAR's performance using multiple metrics, including VQAScore, Human Preference Score v2, ImageReward, and Aest... | Summary: This paper presents ZipAR, a training-free framework for accelerating autoregressive visual generation. It leverages the local structure of images by allowing parallel decoding of spatially adjacent tokens, alongside the standard next-token prediction. An adaptive local window assignment with rejection samplin... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: The lack of experiments with 512 or higher resolution.**
For clarity, we would like to highlight that our experimental results already include higher-resolution evaluations, as referred to Table 2 in the paper. Specifically, the LlamaGen-XL ... | Summary: This paper proposes ZipAR, a training-free method to accelerate the decoding speed of the AR image generation model. They first show that significant attention scores are allocated to tokens in the same column of previous rows. Therefore, decoding the next row is not necessary to wait for the finishing of the ... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1: Essential reference VAR is not discussed.**
As noted in our related work section (lines 157-160 in the paper), we do discuss VAR and its approach to visual generation. Moreover, it should be noted that VAR requires specialized multi-scale to... | Summary: This paper introduces a novel technique to conduct parallel decoding in AR-based image generation. The proposed approach can be directly applied to off-the-shelf pretrained AR-based image generation models, speeding up the generation with small performance drop.
## update after rebuttal
Given the updated resu... | Rebuttal 1:
Rebuttal: Thanks to the reviewer for the valuable comments.
**Q1:More diverse automatic evaluation approach should be considered.**
To address this concern, we have expanded our evaluation by assessing ZipAR’s performance using multiple metrics, including VQAScore, Human Preference Score v2, ImageReward, a... | null | null | null | null | null | null |
Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant | Accept (poster) | Summary: The #6131 presents dual feature reduction framework, a novel bilevel screening method specifically for the sparse-group Lasso (SGL) and its adaptive variant (aSGL). SGL works by applying $\ell\_1$ (variable-level) and $\ell_2$ (group-level) shrinkage, and the paper's problem setting minimizes a convex differen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and thought they invested in our manuscript and in providing helpful feedback. In the camera-ready version, we will add the pseudocode from the appendix into the main text to improve readability. In response to specific points raised:
>Although KKT checks preven... | Summary: This paper introduces Dual Feature Reduction (DFR), a novel screening method to enhance the computational efficiency of Sparse-Group Lasso (SGL) and its adaptive variant (aSGL).
DFR applies two-layer screening:
- Group Reduction eliminates inactive groups using a strong screening rule based on dual norms and ... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for taking the time to review our work and for their helpful comments. In the camera-ready version, the experimental section will be restructured to improve readability, reducing the need for frequent cross-referencing. In response to specific points raised:
>Claims ... | Summary: This paper introduces a new feature reduction method in order to improve the computational complexity in solving Sparse-Group Lasso (SGL) problems.
The Dual Feature Reduction (DFR) method that is presented relies on two screening stages (one for inactive groups and another for inactive variables within a group... | Rebuttal 1:
Rebuttal: We want to thank the reviewer for their time in reviewing our work and for their helpful and positive feedback. As suggested, we will increase the font sizes in the figures in the camera-ready version. With regards to your question:
>Why is the GAP Safe approach not included in the evaluations of... | null | null | null | null | null | null | null | null |
RocketKV: Accelerating Long-Context LLM Inference via Two-Stage KV Cache Compression | Accept (poster) | Summary: RocketKV is a training-free KV cache compression strategy designed to optimize the inference efficiency of long-context LLMs during the decode phase. The main challenge it addresses is the exponential memory overhead due to KV cache storage, which scales with sequence length. The method is empirically validate... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback and finding RocketKV results promising.
**Novelty: RocketKV is SnapKV + QUEST:**
As discussed in the paper, existing methods for KV cache compression typically fall into two categories: permanent KV token eviction and dynamic KV token selection. ... | Summary: This paper combines the advantages of permanent KV token eviction and dynamic KV token selection. It uses a two-staged kv cache compression method to give strong results and shows that it reduces GPU memory usage.
Claims And Evidence: I mostly agree with it. However, given that "permanent KV token eviction" c... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our work interesting and providing valuable suggestions.
**Add More Benchmarks to Prove the Losslessness:**
We would like to clarify that RocketKV is not a lossless approach, as evident from the provided accuracy results. And none of the other methods we compared... | Summary: This paper introduces RocketKV, a two-stage KV cache compression approach. The first stage applies permanent KV eviction through adaptive pooling and GQA-compatible SnapKV methodology, while the second stage efficiently retrieves necessary KV components dynamically based on queries via a hybrid attention mecha... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive review and providing constructive feedback.
**Lack of Methodology Explanation:**
Due to Space constraints, we decided to prioritize RocketKV’s performance results, resulting in a briefer method explanation. In the final version, we will move certain ablation studi... | Summary: This paper presents RocketKV, a method that leverages observation made upon existing permanent and dynamic token eviction. Specifically, RocketKV aims to conduct a permanent eviction with a large budget first and refine it to target a budget with fine-grained dynamic evictions.
Claims And Evidence: Yes.
Meth... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful feedback and recognition of our work in several aspects.
**Needle Dataset:**
Our needle dataset already follows reference [2] mentioned by the reviewer which adopts PGraham Essay as background and a passkey-like needle as explained in Appendix A.2... | null | null | null | null | null | null |
Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction | Accept (poster) | Summary: The paper addresses the challenging task of accurately forecasting high-dimensional chaotic systems using a transformer-based approach. By leveraging ergodicity and modifying attention mechanisms, the proposed framework effectively handles high-dimensional chaotic dynamics while preserving long-term statistica... | Rebuttal 1:
Rebuttal: We genuinely appreciate the reviewer’s time and effort in enhancing our paper.
[Link for results and references](https://anonymous.4open.science/r/ChaosMeetsAttention/README.md)
[Backup link](https://filebin.net/37p4dxup0t320143)
* Concern 1 & Question 5:
`The fair comparison in terms of mode... | Summary: The paper introduces a transformer-based model for predicting long-term trajectories in high-dimensional chaotic systems. It modifies standard attention mechanisms using Axial Mean-Max-Min (A3M) attention with random Fourier features to capture spatial correlations. It uses a unitary-constrained loss (based on... | Rebuttal 1:
Rebuttal: We sincerely thank reviewer ARTA for carefully reviewing our manuscript, providing valuable feedback and reconzing the strengths of our work. We'd like to address your concerns in the initial review and answer you questions as follows:
[Essential Link for results and references to this Rebuttal](... | Summary: The paper investigates the problem of predicting the evolution of ergodic chaotic systems with transformers. To that end, the paper introduces a set of modifications to the traditional transformer architecture that overcome crucial bottlenecks in terms of scalability. Moreover, the paper introduces a novel reg... | Rebuttal 1:
Rebuttal: We sincerely appreciate reviewer FGZz for taking the time to thoroughly review our manuscript, offering valuable feedback, and acknowledging the strengths of our work, particularly in the areas of efficient transformers for large-scale chaos systems, physics-inspired regularisation terms, and the ... | Summary: The paper introduces a transformer-based framework for predicting large-scale chaotic systems. The authors tackle a key challenge in dynamical system forecasting -- the amplification of prediction errors due to positive Lyapunov exponents -- by using ergodicity. Their approach includes:
- A modified attention... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's constructive feedback and recognition of our work. We address the concerns and questions as follows:
[Link to visualizations and references](https://anonymous.4open.science/r/ChaosMeetsAttention/README.md)
[Backup](https://filebin.net/37p4dxup0t320143)
* Con... | null | null | null | null | null | null |
DSBRouter: End-to-end Global Routing via Diffusion Schr\"{o}dinger Bridge | Accept (poster) | Summary: DSBRouter is an end-to-end neural global routing solver based on the Diffusion Schrödinger Bridge (DSB) model, which learns the forward and backward mapping between initial pins and routing results. It achieves state-of-the-art performance in overflow reduction on ISPD98 and parts of ISPD07, with some cases ac... | Rebuttal 1:
Rebuttal: > **Weakness 1: This work should collect a large batch of routing results as training data, where traditional global routers do not need this process. According to Table 2 and 4, the method is not very efficient.**
Thanks for your valuable comment. Though traditional global routers indeed do not ... | Summary: This paper introduces DSBRouter, a novel global routing (GR) solver leveraging the Diffusion Schrödinger Bridge (DSB) model. The authors aim to address the challenge of ensuring routing connectivity in network prediction results, a persistent issue in learning-based GR methods. DSBRouter learns both forward an... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for thoroughly evaluating our paper and providing insightful and valuable feedback. We are genuinely committed to addressing your concerns and respond to your specific comments below.
> **Question 1: The paper acknowledges that DSBRouter has a longer... | Summary: This paper introduces DSBRouter, an end-to-end neural global routing solver based on the Diffusion Schrödinger Bridge (DSB) model. Traditional learning-based approaches to global routing (GR) often require post-processing heuristics or reinforcement learning to enforce connectivity, leading to inefficiencies. ... | Rebuttal 1:
Rebuttal: We would like to express our sincere gratitude for thoroughly evaluating our paper and providing valuable and constructive feedback. We are genuinely committed to addressing your concerns and respond to your specific comments below.
> **Weakness: Efficiency is a big concern for the proposed metho... | null | null | null | null | null | null | null | null |
Adapting to Linear Separable Subsets with Large-Margin in Differentially Private Learning | Accept (poster) | Summary: In this paper, the authors propose a $(\epsilon,\delta)$ differentially private algorithm for binary linear classification. The risk bound depends linearly on the arbitrary subset of data points $S_{out}$ , which if removed makes the data linearly separable with margin $\gamma$. The algorithm is adaptive as th... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and thoughtful question. We appreciate your recognition of our work. We have corrected all the typographical errors you identified.
Regarding your question, you are indeed correct: $\tilde{w_t}$ in Algorithm 5 should indeed be $w_{t+1}$. We thank the reviewer on... | Summary: The paper addresses the problem of DPERM for binary linear classification. The authors propose an efficient algorithm that achieves an empirical zero-one risk bound of $\widetilde{O}\left(\frac{1}{\gamma^2 \varepsilon n}+\frac{\left|S_{\text {out }}\right|}{\gamma n}\right)$. The algorithm is highly adaptive, ... | Rebuttal 1:
Rebuttal: # Regarding your comments
> "The experiments in the introduction....Additional explanation would be helpful."
Thanks for asking this insightful question. Before presenting our explanations, we want to clarify that the “normalized margin”, labeled in the y-axis of Figure 2, measures the distance b... | Summary: This paper studies empirical risk minimization of large (geometric) margin half spaces, in the agnostic setting. They have the following major contributions:
a) They give an algorithm for this problem that works even without knowledge of the margin. Prior work by Nguyen et al. (2019) required knowledge of the... | Rebuttal 1:
Rebuttal: # Regarding your comment on weaknesses
> Weakness 1
We thank the reviewer for their valuable feedback. We agree that our work builds upon the JL transform and gradient descent (GD) techniques, which have been explored in prior works such as Nguyen et al. and Bassily et al. However, our algorithm ... | null | null | null | null | null | null | null | null |
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models | Accept (poster) | Summary: The paper proposed MASS, a novel mathematical skill graph construction method for selecting data for pretraining LLMs in the math domain. MASS prompts a strong LLM to generate nodes of skills from a reference dataset, and then construct an adjacency matrix (as a graph) using the dataset statistics. Then the gr... | Rebuttal 1:
Rebuttal: Dear Reviewer 2U9L,
Thank you for your thoughtful feedback and positive recognition. Below, we respond to each of your comments in detail.
1. **Weakness 1:** There is a lack of **theoretical understanding** on why the proposed approach works better than other baselines.
**A:** First, we emp... | Summary: This paper proposes an approach called MASS for selecting mathematical training data. The paper takes a high-quality reference math dataset, obtains each problem's skills (by prompting a LM), and constructs a skills graph, from which we can read off how frequent each skill is in the reference dataset and which... | Rebuttal 1:
Rebuttal: Dear Reviewer CEvW,
Thank you for your thoughtful feedback and positive recognition. Below, we respond to each of your comments in detail.
1. **Weakness 1 Q1 / Question 5:** Does this approach work for **other domains**?
**A:** Yes, it does work for other domains. Please see our reply to re... | Summary: This paper introduces a method for math data selection in pre-training. It begins by extracting a skill graph from a high-quality reference dataset, then utilizes this graph to score a larger dataset and filter out high-quality samples.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes, they eval... | Rebuttal 1:
Rebuttal: Dear Reviewer NxxW,
Thank you for your thoughtful feedback and positive recognition. Below, we respond to each of your comments in detail.
1. **Weakness 1.1:** It would be better to use **stronger base models.**
**A:** Thank you for your suggestion. We chose Qwen2.5-7B as a stronger base mo... | null | null | null | null | null | null | null | null |
Fast Inference with Kronecker-Sparse Matrices | Accept (poster) | Summary: This paper presents the first energy and time benchmarks for the multiplication of Kronecker-sparse matrices. These benchmarks reveal that specialized sparse matrix multiplication implementations spend up to 50% of run time on memory rewrite operations. As a remedy, the authors propose a new tiling strategy fo... | Rebuttal 1:
Rebuttal: Thank you for your review. We address your points below.
1. >Are there any other sparse related works relevant for this benchmark?
The benchmark includes all the relevant baselines we are aware of. The revision will include an additional discussion clarifying how our work relates to a few other ... | Summary: This paper proposes a novel CUDA kernel designed to accelerate neural network inference using Kronecker-sparse matrices. These matrices, characterized by sparsity patterns derived from the Kronecker product, offer a structured alternative to traditional dense matrices in neural networks. By optimizing memory a... | Rebuttal 1:
Rebuttal: Thank you for your review.
# Regarding your questions
1. > End-to-end latency results in ViT
Table 4 already provides an end-to-end latency result, showing a 22% relative time gain on a vision transformer when using the kernel. If you actually meant to ask about the *absolute* measurements in s... | Summary: This paper aims to speedup DNN inference with kronecker-sparse matrices by optimizing GPU memory accesses via customizing the CUDA kernels. The paper has made three key contributions: (1) analyzing the time and energy efficiency of existing implementations for multiplying kronecker-sparse matrices; (2) proposi... | Rebuttal 1:
Rebuttal: Thank you for your review.
# Regarding your questions
1. >How did we measure the time spent on memory rewritings
We compared with the execution time where we removed the permutations/memory rewritings part, i.e. lines 1 and 3 in algorithm 1 (details in appendix B.2).
2. >Implications on other tr... | Summary: The paper "Fast inference with Kronecker-sparse matrices" focuses on optimizing matrix multiplication algorithms for Kronecker-sparse matrices, which are used in neural networks to reduce parameters while maintaining accuracy. The main contributions include:
- Benchmarking and Optimization: The authors benchma... | Rebuttal 1:
Rebuttal: Thank you for your review.
# Regarding your questions
1. > Extending the kernel to other hardwares / Limited Hardware Scope
We translated the CUDA kernel to openCL, so the kernel can now be used on other hardwares such as AMD GPUs or CPUs. The CUDA and openCL codes have also been integrated to... | null | null | null | null | null | null |
Improving Diversity in Language Models: When Temperature Fails, Change the Loss | Accept (poster) | Summary: The paper investigates the impact of temperature scaling on the precision–recall (P&R) trade-off in language models. The authors provide a theoretical analysis showing that while lowering the temperature enhances precision, increasing it does not necessarily improve recall. They propose new loss functions (e.g... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and your feedback.
## Decoding-based methods
In our paper, we deliberately focused on the temperature parameter (commonly referred to as the "diversity" parameter) because its theoretical analysis already presented some complexity. For this reason, we chose to l... | Summary: This paper studies how recall and precision can be effectively traded off in language models. First, they study formal definitions of precision and recall in simplified settings and show cases where decreasing the temperature improves precision at the cost of recall, but increasing the temperature hurts both p... | Rebuttal 1:
Rebuttal: We would like to thank Reviewer 2owJ for the detailed review and suggestions. We are glad you appreciate the paper.
Following your suggestion, as well as those from other reviewers, we conducted additional experiments:
**New dataset: MathQA-Python.**
We trained a model on MathQA-Python and use... | Summary: The paper provides a detailed analysis of the relationship between temperature, precision, and recall, offering insights into why lowering the temperature improves quality (precision), while increasing the temperature usually does not enhance coverage (recall). The paper primarily addresses two key questions: ... | Rebuttal 1:
Rebuttal: We thank Reviewer DUE2 for the review and constructive feedback. We have added further experiments with more datasets and models; please refer to our response to Reviewer 2owJ for details. While the first question has only one relevant scenario, we believe the second and third questions require mu... | Summary: Increasing diversity in language models requires careful tuning of decoding temperature. This paper shows that lowering temperature improves precision, but raising it often fails to enhance recall and effective tunability demands training models focusing on coverage. This paper provides two settings where the ... | Rebuttal 1:
Rebuttal: We thank Reviewer pQ4a for the thoughtful feedback and careful review. We are grateful for the time and effort.
First, regarding the algorithmic questions raised by several reviewers: we will include detailed algorithm blocks for all computations in the appendix.
We address explicit questions bel... | null | null | null | null | null | null |
Temperature-Annealed Boltzmann Generators | Accept (poster) | Summary: In this paper, the authors present a temperature-annealing strategy to train normalizing flows to match unnormalized probability densities (in this case focusing on the equilibrium Boltzmann distribution of high-dimensional molecular systems). The training is done with the reverse KL divergence, assuming no ac... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and helpful feedback. We will address your points in the following:
* Comparison to FAB in accuracy and metrics: While Table 1 suggests TA-BG mainly improves in terms of the number of target evaluations, it’s important to note that the high-energy metastable r... | Summary: This paper considers the problem of off-policy sampling from the unnormalized density distributions and proposes a novel method (TA-BG) based on a normalizing flow architecture (like FAB) that is less prone to mode collapse. In fact, the authors present a way of training a normalizing flow in this setting with... | Rebuttal 1:
Rebuttal: Thank you for your detailed and helpful feedback. We address your questions and concerns below:
* While Table 1 suggests TA-BG mainly improves in terms of the number of target evaluations, it’s important to note that the high-energy metastable regions constitute a small part of the state space. Sm... | Summary: The authors propose an iterative training approach for learning normalizing flows to approximate unnormalized densities, such as Boltzmann distributions for physical systems. The method proceeds to first train a normalizing flow using the mode-seeking KL divergence match a high-temperature target density. ... | Rebuttal 1:
Rebuttal: Thank you for your positive, detailed, and constructive feedback.
* We address your points following the same numbering as in your review:
2. We will more clearly outline the advantages of our method. For example, explicitly stating the mass-covering nature of the forward KLD and why this is he... | Summary: This paper proposes temperature annealed Boltzmann generators. The proposed idea is train a normalizing flow with reverse KL at some high temperature, then train a series of models down to the target temperature using forward KL using generated samples reweighted with importance sampling from a higher temperat... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and the many helpful suggestions. We address them in the following:
We performed ablation studies to investigate the impact of hyperparameters, which we will include in our revised manuscript:
* We ran ablation experiments to determine suitable starting temper... | null | null | null | null | null | null |
Customizing the Inductive Biases of Softmax Attention using Structured Matrices | Accept (poster) | Summary: --- score update to 3 (weak accept) from 2 (weak reject) after the rebuttal ---
The paper consider the Score function in attention computation. This is one core component of modern LLMs. The common setting for multi-head attention is that each head has a "low-rank" bottleneck, as the product of key and query ... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We respond to your questions below.
**Relationship with Positional Encoding and RoPE.**
It is certainly true that many modern transformers use positional encoding schemes that capture relative position. For instance, RoPE encodes positional information ... | Summary: --- increased score from 3 to 4 after comment from authors ---
This work proposes a new way to parameterize the query-key operation in attention matrices. When the key and query matrices are low-rank, as it is the case in the vanilla transformer architecture, high-dimensional input data might suffer from a lo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful questions and supportive feedback!
**Hyperparameter and Error Bars.**
In all experiments, for both our methods and baselines, we sweep over the learning rate while keeping other hyperparameters fixed. In all figures, including Fig 3 and Fig 4, we plot only the best... | Summary: The authors propose to address a common limitation of existing softmax attention layers: the information bottleneck when using small head dimension. To do this, they propose to bake in a locality bias into the structured parameterisation of the attention weights.
The attention mechanism is introduced cleanly a... | Rebuttal 1:
Rebuttal: We thank you for your supportive feedback. We address your comments below.
**Larger Models and Datasets:**
We are hopeful about the prospect of scaling up these experiments to even larger models and datasets. As a first step in that direction, we now present a new experiment on hyperparameter t... | Summary: The paper address two issues of the attention computation.
The first is the bottleneck caused by the low rank computation of the Key and Query matrices in the attention computation. Instead of doing the standard low rank decomposition, they proposed to use structure matrices to represent the attention score, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and positive feedback. We address your questions below.
**Contributions in Comparison to Prior Works.**
Thank you for pointing out the connection of our work to the broader literature of structured matrices. We would like to highlight further connections that we di... | null | null | null | null | null | null |
Almost Optimal Fully Dynamic $k$-Center Clustering with Recourse | Accept (poster) | Summary: The paper claims to construct an $O(1)$-approximate solution for the metric $k$-center problem in the dynamic setting, with $O(1)$ amortized recourse and $\widetilde{O}(k)$ amortized update time. By combining a recursively nested MIS (Maximal Independent Set) with a dynamic sparsifier, the paper improves the a... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their efforts and insightful comments. Please let us know if you have any questions about our rebuttal.
Thank you for pointing this out; this will help us improve the clarity of the proofs.
Indeed, since $\beta = O(\log (n/k))$,
there is a $\log (n/k)$ factor hidden... | Summary: This paper gives almost optimal dynamic $k$-center algorithm in metric space. In dynamic $k$-center, the task is to obtain an approximate solution with as small update time and recourse, where recourse means the number of center points needed to be updated per insertion/deletion.
This paper designs an algori... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their efforts and insightful comments. Please let us know if you have any questions about our rebuttal.
It is known that an update time of $\Omega(k)$ is necessary for this problem.
As stated in Line 58 in the introduction, it is known that, in the static setting, a... | Summary: The paper proposes an algorithm for the k-center problem in the fully-dynamic setting in general metric spaces. In particular, the proposed method obtains a constant approximation with constant recourse and Otilde(k) update time (thus the name “almost optimal”). The algorithm is based on a combination of the r... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their efforts and insightful comments. Please let us know if you have any questions about our rebuttal.
Our algorithm can be implemented to have an update time of $O(k \cdot \log^4 n \log \Delta)$ by using standard data structures.
Regarding the approximation ratio ... | Summary: There is no better summary of the paper than the one given in the abstract of the paper. So, I'll simply copy it below:
_"We give a simple algorithm for dynamic k-center that maintains an O(1)-approximate solution with O(1) amortized recourse and ˜O(k) amortized update time, obtaining near-optimal approximati... | Rebuttal 1:
Rebuttal: We thank all the reviewers for their efforts and insightful comments. Please let us know if you have any questions about our rebuttal.
In order to keep the proofs simple, we did not optimize the constant in the approximation ratio in the paper. By carrying out a more intricate analysis of the spa... | null | null | null | null | null | null |
Instruct2See: Learning to Remove Any Obstructions Across Distributions | Accept (poster) | Summary: This paper tackles the problem of obstruction removal in images with transformer-based generative models. The key design lies in the modeling of obstructions and also the alignment between types of obstructions and language descriptions (e.g. "rain drops", "fences"). The learned model achieves comparably bette... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer zzKc's valuable feedback. Our responses to the weaknesses and questions are listed below.
**R-W1 Minor performance improvements:** We would like to emphasize that the primary goal of our method is not to maximize performance for any specific obstruction type, but ... | Summary: This paper introduces a zero-shot framework for image restoration that can handle a wide range of obstructions, including those not seen during training. Overall, the paper contributes a flexible, distribution-agnostic method for obstruction removal that harnesses multi-modal cues and dynamic masking to achiev... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 4Rdn's valuable feedback. Our responses to the weaknesses and questions are listed below.
**R-W1 Limited scope of experimentation:** Thank you for your comment. We believe some of our experimental results may have been inadvertently overlooked. In both the main pa... | Summary: In this paper, the author propose the Instruct2See, which is a zero-shot framework for removing both seen and unseen obstructions from images. It formulates obstruction removal as a soft-hard mask restoration problem, integrating multi-modal prompts via cross-attention. A tunable mask adapter refines masks for... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 91FV's valuable feedback. Our responses to the weaknesses and questions are listed below.
**R-W1 Domain restributions:** Our model is trained on natural image datasets, and as such, it performs well across a wide range of domains within the natural image space, su... | Summary: The paper provides a novel pipeline for “obstruction removal”: the combined task of identifying unwanted obstructions in an image and filling in the masked regions with plausible pixels. The method proposes a) using a hybrid adaptable masking strategy to identify the occluders and b) learning a text and vision... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer 17U3's valuable feedback. Our responses to the weaknesses and questions are listed below.
**R-W1 Lack of key details:** We emphasize that our method targets a fundamentally different problem than prior approaches such as Restormer and PromptIR. These existing rest... | Summary: This paper studies obstruction removal of 2D images. The proposed model is a zero-shot method that can handle both seen and unseen obstacles in open vocabulary. The method is to obtain a mask of the obstructions and inpaint/repaint the image with a transformer. The results are claimed to be state-of-the-art.
... | Rebuttal 1:
Rebuttal: We sincerely appreciate Reviewer CuLC's valuable feedback. Our responses to the weaknesses and questions are listed below.
**R-W1 Mask detector design:** This concern appears to stem from a misunderstanding. We have already clarified the role and details of the mask detector in Appendix A.1 of th... | null | null | null | null |
Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity | Accept (poster) | Summary: This paper studied federated learning where each client has a different computation resource.
The authors first showed that it is optimal to run Asynchronous SGD on the fastest $m^\star$ clients. (Theorem 2.1)
This naive approach is optimal, but it does not work well when the computation power of each client ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the review.
> This paper studied federated learning where each client has a different computation resource.
Our setup is relevant not only to Federated Learning but also to datacenter environments, where heterogeneous GPU clusters are common. Even in datacenters with id... | Summary: This paper discusses the characteristics of asynchronous parallel algorithms when a delay upper bound is provided, specifically Algorithm 4 and Algorithm 5. Asynchronous parallel SGD was the focus of SGD research from 2014 to 2020. With the popularity of the Adam algorithm, research on SGD has begun to decline... | Rebuttal 1:
Rebuttal: Thank you for the review.
> Asynchronous parallel SGD was the focus of SGD research from 2014 to 2020. With the popularity of the Adam algorithm, research on SGD has begun to decline.
We respectfully disagree with this statement. One of the important works [1] in stochastic optimization (a field... | Summary: This paper proposes a method called Ringmaster ASGD to achieve the optimal time complexity for asynchronous methods as described in [1]. Ringmaster ASGD is a simple modification of vanilla asynchronous SGD. In Ringmaster ASGD, gradients with large delays (>R) are discarded.
[1] Tyurin, A. and Richt´arik, P. O... | Rebuttal 1:
Rebuttal: Thank you for the review.
> As shown in Theorem 4.2 and Theorem 5.1, the value of the delay threshold does not depend on the computation times. Does this imply that the same value of R can be used across different distributed systems? This conclusion is somewhat confusing and counter-intuitive. I... | Summary: In the settings when all clients compute the same function, the paper introduces a family of Asynchronous SGD algorithms:
-- A trivial algorithm which chooses the optimal number of fastest machines. The paper shows that this algorithm achieves the optimal convergence rate. The downside of the algorithm is tha... | Rebuttal 1:
Rebuttal: Thank you for the review.
> The techniques feel incremental, with the main proof being completed mainly using Lemmas from Koloskova et al (2022).
We acknowledge that the proof is not complicated, but we see this as an advantage rather than a limitation. A small yet impactful change can often be ... | null | null | null | null | null | null |
Variational Rectified Flow Matching | Accept (poster) | Summary: This paper introduces a variational rectified flow matching method. Instead of learning a deterministic mean velocity at time $t$, the paper explicitly models a distribution over the velocity $v_t$, grounding the approach in VAE theory.
Claims And Evidence: Partially, the baselines do not include recent metho... | Rebuttal 1:
Rebuttal: Thanks for detailed feedback and for recognizing the importance of addressing ambiguity in the marginal velocity field, and our strong results.
**1. Paper's unique contributions**
We study a method for capturing multi-modal velocity vector fields. We show that incorporating an unobserved continu... | Summary: # Update
In the rebuttal, the authors have addressed many of my questions and criticism. I feel that the main issue has not been adequately addressed, so I decided not change the score.
To elaborate, I feel that the method adds complexity to diffusion models. The added complexity has to serve some purposes i... | Rebuttal 1:
Rebuttal: Thanks for feedback and for highlighting our theoretical contributions and strong results across datasets and models.
**1. Quantify the average curvature of 2D trajectories**
We calculated the curvature for 2D data results (**Sec 4.2**) and find significantly lower curvature for our method:
| ... | Summary: The paper introduces Variational Rectified Flow Matching (VRFM), a novel approach that integrates techniques from Rectified Flow Matching (RFM) and Variational Autoencoders (VAEs). This design aims to address the vector ambiguity issue inherent in the original RFM method. Through extensive experiments, the aut... | Rebuttal 1:
Rebuttal: Thanks a lot for your detailed feedback and for recognizing our well-written paper, extensive experiments, and comprehensive evaluation of performance. We also appreciate the acknowledgment of V-RFM’s effectiveness in addressing velocity ambiguity and its superior performance. Below, we address qu... | Summary: This paper proposes Variational Rectified Flow Matching (V-RFM), a generative model that integrates Variational Autoencoders (VAEs) with Rectified Flow Matching (RFM). Unlike conventional RFM, which struggles to capture the multimodal nature of the ground-truth velocity vector field and learns only a single av... | Rebuttal 1:
Rebuttal: Thanks a lot for your detailed feedback and for recognizing V-RFM's novelty and promise as a generative model. See below for answers to your questions:
**1. Training stability of V-RFM. VAE training is not as stable as RFM because it may encounter the posterior collapse problem.**
Great questio... | null | null | null | null | null | null |
SCISSOR: Mitigating Semantic Bias through Cluster-Aware Siamese Networks for Robust Classification | Accept (poster) | Summary: This paper presents a novel debiasing architecture that leverages siamese networks and clustering techniques to mitigate spurious correlations in learned embeddings. The proposed approach remaps the embedding space to discourage unwanted dependencies between inputs and outputs while preserving meaningful seman... | Rebuttal 1:
Rebuttal: **1. On Clustering Method (with Larger Models)**
We conducted an ablation experiment using DBSCAN with LLaMA3, the largest model we used. The results are as follows:
| Dataset | ACC (GYAFC) | F1 (GYAFC) | | ACC (Yelp) | F1 (Yelp) |
|---------------|------------|------------|--|----------... | Summary: ## Summary
This work aims at learning an adapter on pretrained representation to "filter out classification-irrelevant semantic features", to help out-of-distributation robustness. The author proposes a compliciated approach incorporating clustering, reweighting, contrastive learning, and creating “intermedia... | Rebuttal 1:
Rebuttal: **1. On the Lack of Novelty**
We respectfully clarify that, to our knowledge, shortcut learning stemming from a semantic imbalance in pre-trained models (which requires unsupervised analysis) is not widely studied. The theoretical reference [1] you mentioned deals with label quantity bias, rather... | Summary: This paper proposes SCISSOR, a debiasing approach that mitigates semantic biases in classifiers by disrupting semantic clusters that create shortcut learning. Using a Siamese network with Markov Clustering, it creates contrastive learning pairs to remap the semantic space, and through experiments, showed stron... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our work. We sincerely appreciate your time and support. We'll address any further questions you should have during the discussion period to improve our paper, and make it through the finish line. | Summary: This work introduces SCISSOR (Semantic Cluster Intervention for Suppressing Shortcut), a Siamese network-based debiasing approach that remaps the semantic space by discouraging latent clusters exploited as shortcuts. Shortcut learning is a critical issue that undermines model generalization to out-of-distribut... | Rebuttal 1:
Rebuttal: **1. We added a comparative experiment with Invariant Risk Minimization (IRM) [1] and will include the results as well the corresponding references in the Related Work section.**
| Dataset | ACC (GYAFC) | F1 (GYAFC) | | ACC (Yelp) | F1 (Yelp) |
|---------------|------------|------------|-... | null | null | null | null | null | null |
An Efficient Search-and-Score Algorithm for Ancestral Graphs using Multivariate Information Scores for Complex Non-linear and Categorical Data | Accept (poster) | Summary: This paper introduces a greedy search-and-score algorithm for Ancestral Graphs (AGs), which contain directed and bidirected edges to account for latent variables. The key innovation is a normalized likelihood score based on multivariate information over ac-connected subsets (subsets of vertices connected throu... | Rebuttal 1:
Rebuttal: Thank you for your positive review and interesting suggestions to improve the manuscript.
We agree that a running example could illustrate the proposed method and its two-step implementation. We will include such an example in the final revised version to outline the main ideas of the method and ... | Summary: This paper proposes a greedy hybrid search-and-score algorithm to learn ancestral graphs from data with some latent variables marginalized out.
For this purpose, the authors first provides an explicit decomposition of the likelihood function of ancestral graphs in terms of multivari-
ate cross-information ove... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. Thank you also for underlining that "the problem studied (greedy learning of graphs involving latent variables) is very crucial and needed in the field" and that "the decomposition over ‘ac-connected’ subsets of variables shows a clear trajectory generalized fro... | Summary: This paper presents a greedy search-and-score algorithm for ancestral graphs with latent variables, using multivariate information for efficiency. Experimental results verify that it outperforms existing methods in causal discovery like M3HC and MIIC.
Claims And Evidence: Yes
Methods And Evaluation Criteria:... | Rebuttal 1:
Rebuttal: Thank you for your careful review and for underlining that the "theoretical claims are solid" and "the contributions have potential for the broader scientific literature, where causal discovery with latent variables is important".
Concerning your question about the generation of the benchmark dat... | Summary: This paper presents a novel search-and-score algorithm for discovering ancestral graphs - a class of graphical models used to represent causal relationships with latent variables. The key contribution is a new normalized likelihood score based on multivariate information measures applied to ac-connected subset... | Rebuttal 1:
Rebuttal: Thank you for your detailed review and for underlying that "the paper is well-structured and easy to follow, with a clear explanation of the proposed method" and that "most claims are supported by clear and convincing evidence".
- Following your suggestions, we have extended our benchmarks to lar... | null | null | null | null | null | null |
Efficient Network Automatic Relevance Determination | Accept (poster) | Summary: The paper introduces **Network Automatic Relevance Determination (NARD)**, an extension of Automatic Relevance Determination (ARD) designed for linearly probabilistic models. NARD aims to simultaneously model sparse relationships between input features X and output responses Y, while capturing correlations amo... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback and constructive comments. We appreciate your positive remarks on the novelty of our methods and the clarity of the paper.
>Dataset diversity
We have expanded our experiments to include 2 non-biological datasets: Kaggle’s air quality dataset (https://archive.... | Summary: This paper introduces the Network Automatic Relevance Determination (NARD) framework for linearly probabilistic models. It proposes three novel algorithms, i.e. Sequential NARD, Surrogate NARD, and Hybrid NARD, which significantly reduce the computational complexity. These methods maintain comparable performan... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback. We appreciate your positive remarks and the concerns raised, which will help us refine both the theoretical and empirical aspects of our work.
>Baseline comparison
As suggested, we add additional baseline methods: CAPME[1] and JRNS[2]. MRCE and CAPME are f... | Summary: This paper introduces Network Automatic Relevance Determination (NARD), an extension of Automatic Relevance Determination (ARD) designed for multiple-output regression in high-dimensional settings. NARD integrates a matrix normal prior with a sparsity-inducing mechanism to simultaneously select relevant input ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback on our submission.
>Edge case of Theorem 3.1
Recall the Theorem 3.1, $s_i$ is called the sparsity and $q_i$ is known as the quality of $\varphi_i$, The sparsity measures the extent to which basis function overlaps with the other basis vector... | null | null | null | null | null | null | null | null |
Lifelong Learning of Video Diffusion Models From a Single Video Stream | Reject | Summary: This work proposes learning a video diffusion model from a single video stream using lifelong learning, specifically through experience replay. The results demonstrate improved performance compared to standard diffusion training. Additionally, three benchmarks are introduced to support the experiments.
Claims... | Rebuttal 1:
Rebuttal: Before addressing specific concerns, we note that several reviewers have entirely missed the Google Drive links [[a](https://drive.google.com/drive/folders/1ToqSvdFsXJm0UqJZlRURI1uwsIvbHYHn),[b](https://drive.google.com/drive/folders/1IopUyb98v0ybqlaCtimayc9RXnMG63Y-),[c](https://drive.google.com/... | Summary: This work shows that autoregressive video diffusion models can be effectively trained from a single continuous video stream, matching the performance of standard offline training given the same number of gradient steps. The authors further demonstrate that this result can be achieved using experience replay wi... | Rebuttal 1:
Rebuttal: Before addressing specific concerns, we note that several reviewers have entirely missed the Google Drive links [[a](https://drive.google.com/drive/folders/1ToqSvdFsXJm0UqJZlRURI1uwsIvbHYHn),[b](https://drive.google.com/drive/folders/1IopUyb98v0ybqlaCtimayc9RXnMG63Y-),[c](https://drive.google.com/... | Summary: This work investigates ability to learn a video diffusion model in non-iid setting - from a single continuous video stream.
The overall method is an autoregressive UNet-based video diffusion model trained with a continuous stream of data and equipped with a replay buffer. Authors also introduce a collection o... | Rebuttal 1:
Rebuttal: Before addressing specific concerns, we note that several reviewers have entirely missed the Google Drive links [[a](https://drive.google.com/drive/folders/1ToqSvdFsXJm0UqJZlRURI1uwsIvbHYHn),[b](https://drive.google.com/drive/folders/1IopUyb98v0ybqlaCtimayc9RXnMG63Y-),[c](https://drive.google.com/... | Summary: This study shows that autoregressive video diffusion models can be effectively trained from a single, continuous video stream, matching the performance of standard offline methods given the same number of gradient steps. The key lies in using experience replay that retains only a subset of preceding frames. Ad... | Rebuttal 1:
Rebuttal: Before addressing specific concerns, we note that several reviewers have entirely missed the Google Drive links [[a](https://drive.google.com/drive/folders/1ToqSvdFsXJm0UqJZlRURI1uwsIvbHYHn),[b](https://drive.google.com/drive/folders/1IopUyb98v0ybqlaCtimayc9RXnMG63Y-),[c](https://drive.google.com/... | null | null | null | null | null | null |
Bivariate Causal Discovery with Proxy Variables: Integral Solving and Beyond | Accept (poster) | Summary: This paper aims to test the conditional independence relation 𝑋⊥𝑌∣𝑈 in the presence of an unobserved latent confounder U. Due to the unobservability of U, this conditional independent relation cannot be directly tested. The authors show that conditional independence can still be assessed using a proxy varia... | Rebuttal 1:
Rebuttal: We appreciate your efforts and valuable suggestions in reviewing our paper. We address your concerns below.
**Q1.** The method relies on a partially known prior structure, specifically that $U$ causes both $X$ and $Y$, and that $Z$ and $W$ serve as proxy variables for $U$. This assumption may be... | Summary: The paper concerns bivariate causal discovery in the presence of unobserved confounders with the assumption that certain "proxy variables" are observed. Existing literature translates the absence of a direct effect of treatment on outcome to the existence of a solution to a certain integral equation and propos... | Rebuttal 1:
Rebuttal: We appreciate your efforts and suggestions in reviewing our paper. We address your concerns below.
**Q1.** About the type I error level in Liu's paper.
**A.** Liu's type-I error control requires a Lipschitz smooth function, but sqrt does not satisfy this. To make it hold, we conduct experiments... | Summary: The paper proposes a nonparametric procedure for bivariate causal discovery for determining $X \perp Y \mid U$, where $U$ is an unmeasured confounder of $X$ and $Y$. It introduces the Proxy Maximum Characteristic Restriction (PMCR) method to solve an integral equation where a proxy variable is available to det... | Rebuttal 1:
Rebuttal: Thank you for the positive assessment and valuable suggestions about our paper. We address your questions below. We will correct typos and make the number of lemmas consistent in the updated version.
**Q1.** How sensitive is PMCR to kernel and bandwidth choices?
**A.** We would like to clarify t... | null | null | null | null | null | null | null | null |
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures | Accept (poster) | Summary: The paper studies several network design problems, including maximum weight matching, maximum TSP, and subgraph diversity on Euclidean doubling space.
In brief, the paper shows the Gaussian JL dimensionality reduction with $O(\lambda \log{(1/\epsilon)} / \epsilon^2)$ dimensions where $\lambda$ is the doubling... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments. We address the weaknesses they mentioned below.
**Significance of the contribution:** We give an example for obtaining a significantly faster algorithm for estimating weighted matchings, but similar examples exist for all the other pro... | Summary: The paper studies randomized dimensionality reduction for a range of the Euclidean optimization problems, including max-matching,
max-spanning tree, max TSP, max k-coverage, and subgraph diversity. In particular, the paper relates the target dimension to the doubling dimension $\lambda_X$ of the dataset $X$ an... | Rebuttal 1:
Rebuttal: > However, it seems to me they only give an lower bound, which corresponds to the special case (lambda = log n) but not for a general range of lambda?
We thank the reviewer for their careful reading and comments.
The lower bound can be extended to general $\lambda$ as follows. For every $\lambda... | Summary: This paper studies randomized dimensionality reduction for geometric optimization problems such as max-matching, max-TSP, and max-spanning tree. It introduces a novel approach where the reduction is based on the doubling dimension of the dataset instead of the dataset size. The authors prove that reducing the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments. We address the weaknesses they mentioned below.
**1) Datasets:** We focused on datasets that have been used in prior empirical studies on diversity maximization and dimensionality reduction (Tenenbaum et al., 2000; Naeem et al. 2020). ... | null | null | null | null | null | null | null | null |
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks | Accept (poster) | Summary: This paper proposes a simple yet highly effective training algorithm (REST) to effectively reduce feature staleness. The proposed REST significantly improves performance and convergence across varying batch sizes, especially when staleness is predominant. Experiments demonstrate that REST achieves a 2.7% and 3... | Rebuttal 1:
Rebuttal: **Anonymous link: https://anonymous.4open.science/r/REST_ICML2025-0972/REST_ICML_2025_Reviewer_y9S9.pdf**
**A1** Counterexample of Theorem 3.1
We sincerely appreciate the reviewer's careful and thoughtful comments, and we are happy to clarify this counterexample.
Based on our understanding of t... | Summary: This paper presents a simple yet effective training approach called REST for scaling GNNs. The authors analyze the issue of embedding staleness in historical embedding methods, demonstrating that stale features negatively impact model convergence and performance. REST addresses this issue by decoupling forward... | Rebuttal 1:
Rebuttal: **Anonymous link**: https://anonymous.4open.science/r/REST_ICML2025-0972/REST_ICML_2025_Reviewer_1EQa.pdf
**A1** Proof of Theorem 3.2
Please check the detailed proof in the anonymous link . We would like to clarify that the main result in Theorem 3.2 shares foundational assumptions with previous... | Summary: The paper studies the problem of scaling the use of graph neural
networks to large graphs. Existing techniques make use of the
historical features, which may become outdated. On the contrary,
the paper introduces REST algorithm, which contains the
influence of the outdated features. Also, the new model
can... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful comments and thoughtful questions.
**A1** Other Metrics of performance:
We choose to use accuracy as the metric in our submission since all other baselines use it, ensuring a fair comparison. Since the major task is a multi-class classification problem, we... | Summary: In this paper, the author proposes an algorithm to mitigate the issue of feature staleness.
## update after rebuttal
I would thank the author for the rebuttal, and my score remains the same.
Claims And Evidence: The claims appear to be valid.
Methods And Evaluation Criteria: N/A
Theoretical Claims: The gi... | Rebuttal 1:
Rebuttal: **A1** Memory Efficiency:
First, we highlight that REST consistently demonstrate significant advantages over GraphSAGE in terms of memory efficiency across all datasets as shown in Table 4 and 5.
When comparing REST with GAS and VR-GCN, REST maintains a similar memory cost. This is because our w... | null | null | null | null | null | null |
ConceptAttention: Diffusion Transformers Learn Highly Interpretable Features | Accept (oral) | Summary: The paper introduces a method that repurposes the attention mechanisms of multi-modal diffusion transformers (DiTs) to generate highly precise and interpretable saliency maps. Instead of relying solely on traditional cross attention, CONCEPTATTENTION leverages both cross and self attention in the output space ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. After reading all of the reviews, we have implemented many of the requested experiments at [this anonymous website](https://concept-attention-anonymous.github.io/) and we will incorporate these updates into the camera ready paper. We are glad that the review... | Summary: - This paper presents ConceptAttention, a method that leverages the attention of diffusion transformers (DiTs) to generate saliency maps for localizing textual concepts in images.
- By repurposing pre-trained DiT's attention weights, the approach produces more accurate segmentation maps without requiring extr... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments. After reading all of the reviews, we have implemented many of the requested experiments at [this anonymous website](https://concept-attention-anonymous.github.io/) and we will incorporate these updates into the camera ready paper. We are glad that the review... | Summary: The paper introduces ConceptAttention, a novel method for generating saliency maps based on user-defined textual concepts. These maps are of high quality and achieve state-of-the-art performance on zero-shot image segmentation benchmarks, surpassing other interpretability methods. Notably, ConceptAttention doe... | Rebuttal 1:
Rebuttal: We thank the reviewer for their insightful comments. After reading all of the reviews, we have implemented many of the requested experiments which you can see at this [anonymous website](https://concept-attention-anonymous.github.io/). We are glad to see that multiple reviewers recognize the stren... | Summary: The authors present a new method to extract well-refined saliency maps from pre-trained DiT models without having to perform any additional training, mainly by directly leveraging the attention weights of the multi-modal model in a clever way to establish correspondences to a set of provided ‘concepts’ that mi... | Rebuttal 1:
Rebuttal: Thank you for your thorough response. After reading all of the reviews, we have implemented many of the requested experiments at [this anonymous website](https://concept-attention-anonymous.github.io/) and we will incorporate these updates into the camera ready paper. We are glad that the reviewer... | null | null | null | null | null | null |
Learning Compact Semantic Information for Incomplete Multi-View Missing Multi-Label Classification | Accept (poster) | Summary: The paper develops a model named COME for incomplete multi-view missing multi-label classification tasks. Unlike most existing methods, the approach aims to learn compact semantic information by minimizing task-independent redundant information. Additionally, a dual-branch soft pseudo-label generation strategy... | Rebuttal 1:
Rebuttal: Thanks for your constructive reviews and suggestions. Below, we will address each of your questions.
> Q1: Some minor mistakes should be revised, the reference of equation 14 in line 205 is incorrect and it should be capitalized in line 374: “In table 1”.
Thanks for your correction. We have corre... | Summary: The method COME is developed to address the missing data in multi-view multi-label classification tasks, which pursues the maximization of cross-view information to compress the irrelevant information and develops a pseudo-label filling strategy to handle the unavailable labels. Besides, the authors claim that... | Rebuttal 1:
Rebuttal: We greatly appreciate your thoughtful and detailed feedback, and we will address your questions one by one.
> Q1: It would be beneficial for the authors to clarify through experiments why dual-branch model is preferred compared to a single-branch structure.
Thank you for your suggestions. We carr... | Summary: The authors delve into the study of incomplete Multi-view Missing Multi-Label Classification (iM3C) and aim to address the inadequacy of contrastive learning in dealing with incomplete multi-view data and the negative impact of missing labels. To tackle this problem, they propose a consistent semantic represen... | Rebuttal 1:
Rebuttal: Thank you for your valuable review. We will address your questions one by one.
> Q1: Three important hyperparameters in equation 13, $\lambda_1$, $\lambda_2$, $\beta$, need the subsequent experiment to analyze their impact.
Thank you for the suggestion. In Appendix D, we investigate the impacts o... | Summary: This paper proposes a multi-view multi-label learning approach by integrating compact semantic information learning and pseudo-labeling imputation to address the degenerative multi-view contrastive learning and missing label issues. The authors elaborate the failure of multi-view contrastive learning in view a... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions! Below, we will address each of your questions.
> Q1: Providing comprehensive experimental details (e.g., dataset-specific hyperparameters) or releasing the source code would significantly enhance the transparency of this work.
Thank you for your suggestion... | null | null | null | null | null | null |
Sparse Causal Discovery with Generative Intervention for Unsupervised Graph Domain Adaptation | Accept (poster) | Summary: This paper studies unsupervised graph domain adaptation from a causal perspective. The authors claim that existing methods fail to achieve optimal performance due to the entanglement of causal-spurious features. To address this issue, the authors proposed SLOGAN for graph classification domain adaptation by sp... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough review. Below, we address each of your concerns in detail.
---
> Q1. Should test more domain split perspectives
Thank you for this valuable suggestion. Our cross-dataset experiments (Tables 1, 3, 4, 6) incorporate natural shift, feature shift, an... | Summary: The paper presents SLOGAN, a novel approach for transferring knowledge from a labeled source domain to an unlabeled target domain on graph data. The key innovation of SLOGAN lies in its three-component framework: sparse causal discovery, generative intervention mechanisms that break local spurious couplings; a... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough assessment of our work and the constructive feedback. Below, we address each of the concerns raised.
---
> 1. Symbols in Eq. 16 not well-defined
We apologize for the lack of clarity in Eq. 16. This equation describes our generative intervention m... | Summary: This paper studies the unsupervised graph domain adaptation problem, which aims to transfer the knowledge learned on labelled data to the data in the target domain with significantly different distribution.
The motivation of the paper is that the existing works cannot obtain satisfying performance due to the ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback. We appreciate the opportunity to clarify these points and improve our paper.
---
> Q1. The code of the method has been released, but the model.py and main.py seems only contain graph neural networks, while the location of the code f... | Summary: This paper proposes SLOGAN, a framework for Unsupervised Graph Domain Adaptation that addresses two key challenges: the entanglement of causal and spurious features, and the failure of global alignment strategies in graph data. SLOGAN constructs a sparse causal graph using mutual information bottleneck princip... | Rebuttal 1:
Rebuttal: We sincerely thank you for your valuable feedback and thoughtful comments. We address each point below:
---
> Q1. While the paper thoroughly discusses prior work on UDA, GNNs, and causal representation learning, it overlooks several recent works that integrate causal inference with domain adapta... | null | null | null | null | null | null |
Puzzle: Distillation-Based NAS for Inference-Optimized LLMs | Accept (poster) | Summary: The paper introduces Puzzle, a distillation-based NAS approach for extracting inference-optimized LLMs for existing trained models such as Llama. The authors first introduce the search space for their NAS-based optimizations - including the different attention and FFN subblocks to use, followed by the number o... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback!,
*"For the throughput comparison of table 2, can the authors clarify what batch sizes they used for the numbers?"*
This is a good question. For every model and hardware setting we selected the optimal batch size to get the best throughput per GPU. This was d... | Summary: The paper introduces Puzzle, a hardware-aware framework that optimizes LLM inference efficiency using neural architecture search (NAS), blockwise local knowledge distillation (BLD), and mixed-integer programming. The authors demonstrate its effectiveness with Puzzle-51B, a 51B-parameter model derived from Llam... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback,
*"no compression results for a 7B model"*
*"experiments only focus on compressing a 70B model to 51B"*
We demonstrate Puzzle's robustness by applying it 11 times with varied constraints, datasets, and budgets:
(1) 4 derivatives of Llama-3.1-70B (includi... | Summary: The paper proposes a NAS pipeline for pruning a pre-trained large language model. The search space includes pruning the attention heads for the attention module and pruning FFN columns (intermediate size) for the FFN module. The pipeline includes three pieces: 1) blockwise local distillation: by training each ... | Rebuttal 1:
Rebuttal: Thank you for your feedback and appreciation of the ablation studies,
*"There are several metrics considered for the replace-1-block score, but I don't find where/if the author conducts ablation studies on them and which one is the best"*
Appendix F.1.4. examines the impact of different replace-... | Summary: This paper is concerned with model compression, which aims to compress the scales of LLMs. This paper proposes a NAS framework named Puzzle to conduct easy-to-achieve NAS. The Puzzle framework firstly trains decoupled blocks for each layer via block-wise local distillation, then searches best-fit plan for arch... | Rebuttal 1:
Rebuttal: Thank you for your feedback and suggestion,
*"Key baselines are missing, which 1) uses distillation on a fully random architecture 2) uses distillation on a random-from-block-library architecture."*
We conducted evaluations on the baselines you suggested, namely (1) a fully random architecture a... | null | null | null | null | null | null |
Adapting to Evolving Adversaries with Regularized Continual Robust Training | Accept (poster) | Summary: Most robust training methods focus on specific attack types and struggle to maintain robustness when new attacks arise, making continual robust training (CRT) necessary. This paper proposes a logit-space regularization approach to preserve robustness across both previous and new attacks efficiently, demonstrat... | Rebuttal 1:
Rebuttal: Thank you for your insightful review.
> Discussion of RAMP
Thank you for pointing us to this interesting relevant work. This work looks at achieving robustness against multiple Lp norms and proposes a logit pairing loss which aims to minimize the KL divergence between the logits of predicting o... | Summary: The paper presents a regularization method for robust continual learning and evaluates it using extensive experiments.
Claims And Evidence: The author claims ALR is essential for maintaining robust performance, while from the experiment section, it seems adding ALR sometimes may not even be the optimal, while... | Rebuttal 1:
Rebuttal: ## General clarifications
*Goals in CAR*
We optimize three objectives (Def 2.1): (1) robustness to known attacks, (2) robustness to unforeseen attacks, and (3) update efficiency. (Known) metrics correspond to (1), (all) metrics to (2), and training time to (3). Thus, (known), (all), and time met... | Summary: The paper proposes a algorithm to robustly finetune the model for newly proposed attacks. Specifically, the paper proposes a regularization term called ALR at the both pretraining and finetuning stage. The regularziation term bound the difference between clean logits and the adversarial logits. The experiment ... | Rebuttal 1:
Rebuttal: Thank you for your positive appraisal of the paper and interesting questions.
> What is the function of the regularization term ALR? When using it at the pretraining stage, does it accelerate the funetuning stage or make the initial model more robust to unknown attack? To prove this, it is better... | Summary: This paper introduces Regularized Continual Robust Training (RCRT), a framework for adapting deep learning models to evolving adversarial attacks while maintaining robustness to previously seen threats. The authors theoretically demonstrate that the gap in robustness between different attacks is bounded by log... | Rebuttal 1:
Rebuttal: Thank you for your insightful review and positive appraisal of our paper.
> Discussion of gradual domain adaptation
Thank you for pointing us to this line of work. The work referenced studies shifts in *data distribution over time* and proposes gradual self-training to adapt the source model wi... | null | null | null | null | null | null |
Multi-objective Linear Reinforcement Learning with Lexicographic Rewards | Accept (poster) | Summary: This work focuses on development of an algorithmic framework with theoretical performance guarantees in Multi-objective RL where the underlying Multi-Objective Markov Decision Process (MO-MDP) is assumed to be linear. The algorithmic strategy optimizes for lexicographic rewards which are essentially hierarchic... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback. We have carefully considered your concerns (including Weaknesses and Questions raised), and our responses are provided below.
---
*W1. The lack of experimental results is a significant limitation.*
Thank you for raising this issue. The absence... | Summary: This paper studies multi-objective RL (MORL) with lexicographic rewards in linear MDPs, where rewards comprise hierarchically ordered objectives. A key challenge in MORL is the failure of Bellman optimality. They propose the LLRL algorithm and establish the first regret bound for MORL under a certain assumptio... | Rebuttal 1:
Rebuttal: Many thanks for your constructive reviews. We have carefully considered your concerns and our responses are provided as follows.
---
*Q1. What is the definition of $a_2$ lexicographically dominates $a_1$ in Assumption 1? Does it mean the reward vector $[r_h^1(x, a_2),\cdots,r_h^m(x, a_2)]$ lexic... | Summary: This paper studies linear Markov Decision Processes (where the transition function and reward function can be expressed using a known linear kernel and two unknown vectors). The paper introduces a novel algorithm for finding policies according to a lexicographic objective with bounded regret. While prior work ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough and constructive feedback. We have carefully considered each weaknees and present our responses below, which will be incorporated into the revised paper.
---
*W1. To aid with comparison to existing work investigating lexicographic objectives (not-necessarily... | Summary: This paper provides an algorithm for the multi-agent reinforcement learning (MORL) setting and regret bounds. Notably the regret bounds given match the single-objective setting up to the leading order term.
## update after rebuttal
I found the work to be making a substantial contribution. I am glad to see t... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback and have carefully considered the raised concerns. Our point-by-point responses follow below.
---
*Q1. **Claims And Evidence:** Contributions 1-3 are well supported and insightful. However, the finite-horizon assumption shouldn't be glossed over ... | null | null | null | null | null | null |
Residual Matrix Transformers: Scaling the Size of the Residual Stream | Accept (poster) | Summary: The Residual Matrix Transformer (RMT) replaces the residual stream in transformers with an outer product memory matrix, allowing independent scaling of the residual stream size. This results in improved training efficiency and better performance on downstream tasks.
Claims And Evidence: Yes
Methods And Evalu... | Rebuttal 1:
Rebuttal: We thank Reviewer ciWT for their comments and provide responses to their concerns about runtime efficiency and baseline comparisons.
For concerns about runtime, we will copy and paste a relevant snippet of our response to reviewer J7dP. We encourage Reviewer ciWT to read our discussion with Review... | Summary: The paper introduces Residual Matrix Transformers (RMT), which increases the size of the residual stream in a transformer without incurring significant compute over memory overhead by using an outer product memory matrix. In training GPT-2 language models, RMT achieves better loss per unit of compute or parame... | Rebuttal 1:
Rebuttal: We thank reviewer J7dP for their thoughtful reading of the paper and appreciate their comment that this work presents an important research direction. We provide responses to many of their concerns grouped by subject.
We first address the concerns related to our application of µP transfer.
> The... | Summary: To achieve more data-efficient and compute-efficient models, this paper introduces a new transformer-variant called the Residual Matrix Transformer (RMT), which replaces the traditional residual stream with an outer product memory matrix. The authors present theory showing that the RMT exhibits efficient scali... | Rebuttal 1:
Rebuttal: We appreciate the reviewer fZLC’s thoughtful feedback and positive comments. We will address each of this reviewer’s concerns in the order they appear.
> The experiments may lack some ablation studies to demonstrate the contributions of different factors. For instance, the authors need to demonst... | null | null | null | null | null | null | null | null |
From Debate to Equilibrium: Belief‑Driven Multi‑Agent LLM Reasoning via Bayesian Nash Equilibrium | Accept (poster) | Summary: The paper introduces ECON, a hierarchical reinforcement learning framework designed to optimize multi-agent reasoning in Large Language Models (LLMs) by leveraging Bayesian Nash Equilibrium (BNE). ECON replaces inter-agent communication with a belief mechanism to save communication costs and make it easier to ... | Rebuttal 1:
Rebuttal: Dear Reviewer t4Dv, thanks for the feedback and suggestions, we will add clarification where needed as space permits.
### Q1:Explain the Implementation of BNE in Sec 2.3
**Regarding the definition of belief**: belief represents each Execution LLM's policy derived from partial observations and lo... | Summary: This paper proposes a Multi-agent LLM framework (ECON) to improve the communication efficiency and consensus. It formulates multi-agent LLM as Decentralized Partially Observable Markov Decision Process. It reduces the token consumption from incomplete-information perspective, and optimizes towards Bayesian Nas... | Rebuttal 1:
Rebuttal: Dear Reviewer hmqe:
Thanks for your constructive review, we provide some response regarding your question:
### Q1: About the inference phase of ECON
> Section 2 mainly focuses on the optimization phase and it is not clear **how the inference phase works in ECON**. What is the observation of each... | Summary: The paper introduces ECON, a hierarchical reinforcement learning framework that optimizes multi-agent reasoning in Large Language Models (LLMs) by leveraging Bayesian Nash Equilibrium (BNE) under incomplete information. By modeling collaboration as a Decentralized Partially Observable Markov Decision Process (... | Rebuttal 1:
Rebuttal: Dear reviewer sVjj, we'd like to thank you for your careful readings and valuable comments, we provide point to point response as follow:
### Q1: The proof of sublinear convergence rate (Appendix B.2)
We acknowledge that the proof in Appendix B.2 would benefit from a more rigorous presentation. ... | Summary: This paper introduces ECON, a multi-agent framework designed to enhance the reasoning capabilities of LLMs. ECON models the multi-LLM setup as a DEC-POMDP with incomplete information, employing a Bayesian Nash Equilibrium perspective. Specifically, multiple “Execution LLMs” reason in parallel, each maintaining... | Rebuttal 1:
Rebuttal: Dear Reviewer 7mra:
Thanks for your constructive review, we provide point to point response regarding your question:
### Q1: The Clear Definition of DEC-POMDP and the ECON Framework
**Reply:** We provide a more intuitive and detailed explanation of our framework below:
----
### Detailed DEC-PO... | null | null | null | null | null | null |
A Market for Accuracy: Classification Under Competition | Accept (poster) | Summary: This paper examines a machine learning (ML) model market for classification tasks. Specifically, the authors assume that multiple ML model providers compete for market share. Each user in the market randomly selects an ML provider that delivers an accurate prediction, and the market share of each provider is d... | Rebuttal 1:
Rebuttal: Thank you for the insightful feedback! We are pleased that you appreciated the soundness of our results and found the empirical results to complement them nicely. We would like to address the questions laid forth in your review, and hopefully alleviate some concerns:
**”Best-response classificati... | Summary: This paper defines a simplified economic model to consider how the dynamics of an “accuracy market” between multiple firms would play out. In this model, each firm has a model that makes predictions about a user, and users are assumed to choose a provider with the highest accuracy. The paper lays out several t... | Rebuttal 1:
Rebuttal: Thank you for your review and efforts. We have addressed your concerns in our response below. As for the issue of whether our paper is a good fit for ICML – which seems to be a main concern – we hope that our response, combined with the other reviews for our paper, help in establishing its relevan... | Summary: This paper studies equilibria in marketplaces when multiple firms are competing with each other for consumers. As compared to the prior literature in this space, this paper focuses on the dynamics of learning an equilibrium between two firms, as well as the impact on consumers and markets. Interestingly, this ... | Rebuttal 1:
Rebuttal: Many thanks for your positive review! We are pleased that you found that the paper was clearly written and that you enjoyed our result showing how competition can induce anti-competitive behavior. In line with this, we would like to address some of your feedback below:
**”It would be helpful if a... | Summary: The paper studies a market where multiple model providers compete to provide accurate predictions to as many users (points) as possible. The theoretical analysis reveals several interesting insights. The main insight is that naively maximizing for accuracy is not optimal for either player. For example, if ther... | Rebuttal 1:
Rebuttal: Thank you for your feedback! We are encouraged that you found the paper well-written and that you appreciate our analysis and experiments.
**”The biggest weakness is the assumption that the players know the predictions of other players…”**
Indeed, our analysis relies on the simplifying assumpti... | null | null | null | null | null | null |
Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment | Accept (poster) | Summary: The authors have introduced a new framework PNBA for aligning neural and behavioural distributional representations. Their approach allows learned and generalisable embeddings across subjects. Their framework uses a multimodal VAE architecture with a constrastive loss term (probabilistic matching term) to prov... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer's thoughtful feedback and the positive assessment. We address each point as follows:
```Q1. The langrangian arguments could do with more detail.```
**A1**: Thanks for this suggestion. In our revision, we will add a more comprehensive derivation of the Lagrang... | Summary: Modeling shared variability across multiple animals is critical to understand universal principles of neural computation. Still, we like probabilistic tools to capture them into a singular representation. This work introduces a new probabilistic method to represent neural and behavioral variability across anim... | Rebuttal 1:
Rebuttal: We thank the positive assessment and constructive feedback. We address each point as follows:
```Q1. Decoding behavior would better demonstrate captured behavioral information. Authors should discuss limitations on real-time inference and computational demands for BCI.```
**A1**: Behavior decodi... | Summary: This work proposed a probabilistic representation alignment framework PNBA that can be used to align neural activities and animal behaviors. The method is applied across brain regions, neural data modalities, and animal species. Authors provided extensive experimental evidence across multiple datasets, validat... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the constructive feedback. We address each point as follows:
```Q1. Line 382-384’s claim is confusing. How is the calcium imaging encoder trained?Is the zero-shot experiments still the cross-subject zero-shot experiments?Is the behavioral encoder frozen?If both... | Summary: This paper introduces PNBA, a framework leveraging probabilistic modeling to find robust preserved representations across different scales of neural variability: trials, sessions, and subjects. The method is evaluated on three datasets spanning M1, PMd and V1 of primates and mice, showing zero-shot preserved r... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's detailed feedbacks! We address each point as follows:
```Q1. How does zero-shot generalization work with varying neuron counts across subjects?```
**A1**: Our model uses convolutional and pooling layers to **standardize input activities of varying counts into a fixed... | null | null | null | null | null | null |
PINNsAgent: Automated PDE Surrogation with Large Language Models | Accept (poster) | Summary: The paper introduces a framework that utilizes LLMs to design and optimize PINNs to solve PDEs. It facilitates solving PDEs with PINNs more efficiently without tuning parameters and choosing architectures manually. The paper demonstrated its effectiveness on dataset PINNacle.
Claims And Evidence: Yes.
Method... | Rebuttal 1:
Rebuttal: # Reply to Reviewer mBxJ
We sincerely thank the reviewer for their thoughtful comments and constructive feedback. We address each concern below and explain how we will improve the manuscript accordingly.
## The Unique Role of LLM in Our Framework
The LLM serves several critical and unique funct... | Summary: This paper introduces PINNsAgent, a framework that uses large language models (LLMs) to automate the development and optimization of Physics-Informed Neural Networks (PINNs) for solving partial differential equations (PDEs). The key components are:
1. Physics-Guided Knowledge Replay (PGKR) – encodes PDE charac... | Rebuttal 1:
Rebuttal: # Reply to Reviewer gzrR
We sincerely thank the reviewer for their thoughtful assessment and insightful questions. We address each point below.
## Baseline Details and Computational Costs
We ran all experiments on 8 NVIDIA V100 (32GB) GPUs, providing sufficient computational resources to compl... | Summary: The paper introduces PINNsAgent, an automated framework using LLM to design and optimize PINNs for solving PDEs. It addresses the limitations of manual hyperparameter tuning by incorporating two novel methods: Physics-Guided Knowledge Replay for efficient knowledge transfer from past experiments, and the Memor... | Rebuttal 1:
Rebuttal: # Reply to Reviewer znat
We appreciate the reviewer's thorough assessment of our paper. Below, we address the key concerns raised:
## Methods and Evaluation Criteria
1. **Knowledge Transfer Evidence**: The reviewer questions whether improvements come from learning genuine transferable knowledge... | Summary: In this work, the authors introduce PINNsAgent, a surrogation framework that leverages large language models (LLMs) enabling efficient knowledge transfer from solved PDEs to similar problems. By leveraging LLMs and exploration strategies, PINNsAgent enhances the automation and efficiency of PINNs-based solutio... | Rebuttal 1:
Rebuttal: # Reply to Reviewer Yfa3
We sincerely thank the reviewer for their detailed assessment of our paper. We address each concern below:
## Novelty and Technical Depth
Our multi-agent LLM framework is the first comprehensive system to fully automate PINNs development without expert intervention. In ... | null | null | null | null | null | null |
Universal Length Generalization with Turing Programs | Accept (poster) | Summary: This work proposes Turing Program, which is a CoT strategy that decomposes an algorithmic task into steps mimicking the computation of a Turing Machine. The work showed that by using Turing Programs, they obtain robust length generalization on a range of algorithmic tasks: addition, multiplication and in-conte... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We respond to the main points raised by the reviewer below.
**Theoretical analysis may be missed:** If the reviewer can further explain what is lacking in terms of the theoretical analysis, we would love to further explain and add to the revision. From ou... | Summary: The paper proposes a new method for designing chain-of-thought supervision for algorithmic tasks, termed "Turing Programs". Essentially, the state of a Turing machine (including the tape, head position, and internal state) before and after each transition are serialized and represented in a chain of thought. T... | Rebuttal 1:
Rebuttal: We thank the reviewer for their feedback. We respond to the main points raised by the reviewer below.
**Key claims of the paper:** we want to emphasize that the key claim of our paper is that Transformers can achieve *length generalization* (generalization to problems longer than the ones observe... | Summary: The paper tackles the challenge of length generalization in transformer models—the ability to extrapolate from short training sequences to test sequences longer. The main contribution is Turing Programs, a novel scratchpad strategy inspired by Turing machine computations. In this framework, an algorithmic task... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We respond to the main points raised by the reviewer below.
**Universality:** We understand that the word “universal” may not accurately capture the nature of our results, and we are open to removing it from the title if the reviewer thinks this ... | Summary: This paper introduces Turing Programs, a novel CoT strategy that improves length generalization on a range of algorithmic tasks. By structuring algorithmic tasks as step-by-step computations resembling a Turing Machine, this method achieves robust generalization across tasks like addition, multiplication, and ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback. We respond to the main points raised by the reviewer below.
**Only algorithmic problems:** The reviewer is right to point out that currently the result is not immediately practical. For this work, our goal is to do a deep evaluation of how length... | null | null | null | null | null | null |
H-Tuning: Toward Low-Cost and Efficient ECG-based Cardiovascular Disease Detection with Pre-Trained Models | Accept (poster) | Summary: This paper proposes H-Tuning, a novel framework that reduces the computational cost of fine-tuning large pre-trained models for ECG-based cardiovascular disease detection by integrating mix-order optimization, low-rank adaptation, and layer-dependent tuning. Additionally, it employs knowledge distillation to t... | Rebuttal 1:
Rebuttal: We are grateful for your insightful comments on our work. In this rebuttal round, we provide an external validation on a wearable ECG dataset, a more thorough ablation study to support the claims of our study. At the same time, experiments on different backbones strengthen the flexibility of the p... | Summary: This paper aims to detect cardiovascular disease by fine-tuning large-scale pre-trained models using ECG signals. It focuses on low-cost and efficient fine-tuning through a mix-order optimization with low-rank adaptation and a novel layer-dependent model update scheme. Then, a knowledge distillation technique ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for all your questions and suggestions.
- **Claims**:
The reasons for choosing the ECG setting: (1) The ECG community offers many open-access datasets for model training and evaluation. (2) Developing low-cost methods for accurate and mobile cardiac healthcare is ... | Summary: The authors propose H-tuning, a model pipeline for efficiently fine-tuning pre-trained models for ECG classification to enable cardiac diagnosis under limited computation resources.
They combine zeroth- order backpropagation, low-rank adaptation, and knowledge distillation to reduce computation times and memo... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for all your questions and suggestions.
- **Methods And Evaluation Criteria:** To assess the generalizability of our classifiers in mobile cardiac healthcare, an external validation set consisting of 7000 wearable 12-lead ECG signals, provided by [1], is used for te... | null | null | null | null | null | null | null | null |
Probabilistic Factorial Experimental Design for Combinatorial Interventions | Accept (spotlight poster) | Summary: This paper studies the combinatorial intervention problem. The authors propose a probabilistic factorial experimental design, where each unit independently receives a random combination of treatments according to specified dosages. They derive a closed-form solution for the near-optimal design in the passive s... | Rebuttal 1:
Rebuttal: We thank the reviewer for finding our extensions enlightening. We find the reviewer's suggestions insightful and accordingly lay out additional experiments and their results.
> Simulation results on real-world dataset are missing.
We thank the reviewer for sharing this concern. Our paper is con... | Summary: This paper introduces probabilistic factorial experimental design for combinatorial interventions, where each treatment is assigned a dosage between 0 and 1, and units randomly receive treatments based on these probabilities.
This framework generalizes both full and fractional factorial designs by allowing ra... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our theoretical analysis, as well as for their many valuable suggestions. Below, we address the reviewer's concerns and lay out modifications we will make according to the reviewer's suggestions.
> My only concern is that all the evaluations are conducted on... | Summary: This paper is concerned with the problem of experimental design in the high dimensional factorial setting where users may be administered combinations of treatments, and the aim is to administer a subset of treatments such that all combinations are recovered. The authors frame this problem in terms of the Four... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our method and the thoughtful suggestions. We would like to address the concerns and questions of the reviewer as below.
> The authors should have a broader literature review of the partial factorial design literature.
We thank the reviewer for this suggest... | Summary: The paper introduces a probabilistic factorial experimental design to address the optimal experimental design problem for combinatorial interventions.
The contribution of the paper:
1. The paper introduces a probabilistic factorial experimental design for a given choice of dosage vector.
2. The paper provides... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our theoretical framework and scalability challenges it addressed. Below, we address the concerns and questions brought up by the reviewer.
> The use of Boolean functions and Fourier transforms is not new, as similar approaches have been explored in prior wo... | null | null | null | null | null | null |
Locally Differentially Private Graph Clustering via the Power Iteration Method | Reject | Summary: The authors study the problem of spectral clustering in local differential privacy (LDP) in the edge-level DP model. Prior work on LDP in the model used the standard randomized response method.
The work is based on the interesting insight that while one wants to compute the second eigenvector; the largest comp... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful consideration of our paper. We sincerely appreciate your comments, which will be very helpful in significantly improving our manuscript. Since your evaluation of our paper is a weak reject, please feel free to let us know if there are any specific comments o... | Summary: This paper considers the problem of graph clustering under privacy constraints. Specifically, the algorithm must satisfy Local Differential Privacy according to Definition 2.2 with budget \epsilon. The authors propose an algorithm, Private Power Iteration Clustering, which approximates B^T x, where x is an ini... | Rebuttal 1:
Rebuttal: Thank you very much for your kindness in accepting to review our paper and in giving several comments that will help improve our manuscript.
> Theorem 4.1 shows that the algorithm satisfies the privacy budget. Some evidence is given that the algorithm returns a similar classification to that of n... | Summary: The authors consider the problem of finding a partition (aka clustering) of a graph privately. The goal is to provide a locally differentially private (LDP) algorithm. There the notion of privacy used in edge privacy, i.e., two neighboring adjacency lists differ by a single edge.
The authors show how to make a... | Rebuttal 1:
Rebuttal: Thank you very much for your kind consideration and comments.
> some of the assumptions are required for the privacy guarantees to hold, while others are only needed for the algorithm convergence or the output quality. For example, are assumptions 2 and 3 needed for Theorem 4.1 to hold?
While ... | Summary: The paper considers graph clustering under edge-level localDP. The work proposes private power-iteration clustering to obtain the partition of nodes. They show that under certain assumptions, this method obtains good approximation to spectral clustering with $O(1)$ valued $\epsilon$. Previous work obtain same ... | Rebuttal 1:
Rebuttal: Thank you very much for your kind consideration and comments. Please kindly find our answers to the comments below.
> Experimental setup and the baseline used are appropriate. The results are convincing, but the baseline for real-world dataset is not provided citing computational issues. I wonder... | null | null | null | null | null | null |
Schwarz–Schur Involution: Lightspeed Differentiable Sparse Linear Solvers | Accept (poster) | Summary: This paper investigates efficient methods for solving sparse linear equations that commonly arise in applications related to partial differential equations (PDEs) and convolutional neural networks. The key insight of the study is the exploitation of hidden structures within convolutional kernels, allowing the ... | Rebuttal 1:
Rebuttal: Thanks for the insightful comments and efforts to verify our method & derivation!
We appreciate the detailed feedback on improving clarity, and will be sure to elaborate on full details to reduce readers’ efforts to reconstruct the steps.
Please also refer to Re:eGtJ for extra descriptions, and ... | Summary: The authors propose an efficient method to solve sparse linear systems. Current algorithms for solving such systems are slow, which hinders their applications in real-time scenarios such as interactive graphics. The authors propose a direct solver, which uses a divide-and-conquer strategy to efficiently solve ... | Rebuttal 1:
Rebuttal: We appreciate the thoughtful feedback on improving our paper. Please also refer to Re:eGtJ for extra descriptions of our methods.
# Method motivations and descriptions
We are committed to improving the exposition and we are confident we can make the paper significantly more accessible to a broader... | Summary: The paper proposes a method for accelerating sparse linear and PDE solvers by transforming sparse Laplacian matrices into dense tensors. This procedure uses dense GPU BLAS kernel to batch and run such system in parallel. This method is differentiable, which can be potentially useful for machine learning pipeli... | Rebuttal 1:
Rebuttal: # General responses
We thank the reviewers for their careful examination of our paper. We note that all reviewers appreciate the broad impact that our method will have (eGtJ: “can be applied to extensive areas”, D4Wa: “greatly accelerate scientific computation in many domains”, QcrL: “both novel a... | null | null | null | null | null | null | null | null |
AAAR-1.0: Assessing AI’s Potential to Assist Research | Accept (poster) | Summary: This paper introduces AAAR-1.0, a benchmark designed to assess the capabilities of Large Language Models (LLMs) in assisting with research-specific tasks. While most existing benchmarks focus on general-purpose tasks, AAAR-1.0 specifically targets high-level academic reasoning and research assistance, addressi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your detailed review and comments. Below, we provide our comprehensive responses to your questions.
---
>Q1. Why LLMs fail on `EQINFER` task & provide the failure patterns discussion.
`EQINFER` leverages a challenging binary inference setting, where LLMs are forced to e... | Summary: This paper introduces AAAR-1.0, a novel benchmark designed to evaluate the ability of Large Language Models (LLMs) to assist researchers in expert-level tasks. The benchmark comprises three distinct tasks: EquationInference (EQINFER), which assesses the LLM's ability to validate the correctness of equations wi... | Rebuttal 1:
Rebuttal: Your comments are very much appreciated! We took your comments carefully and tried to address them one by one.
---
>Q1. The critical references that were missed (i.e., CycleResearch and DeepReview).
Thanks for highlighting these important concurrent works. Specifically, we ran CycleResearch on ... | Summary: The paper introduces AAAR-1.0, a benchmark for measuring the ability of LLMs to perform 3 key research tasks: mathematical equation understanding, designing experiments, and identifying weaknesses in paper submissions.
The authors curate datasets for each of their chosen research tasks by scraping public rese... | Rebuttal 1:
Rebuttal: Thanks for your efforts in reviewing our manuscript! We're glad you found our dataset interesting, the proposed evaluation metrics reasonable, and the experimental results useful. Below, we address your concerns in detail.
---
>Q1. Problem for setting the 'ground truth' for `EXPDESIGN` and `WEAK... | Summary: This paper aims to measure the capability of Large Language Models (LLMs) in research-relevant tasks. Specifically, those tasks include 1) Equation Inference, which measures whether the equation is relevant to the given context of the paper, 2) Experiment Design, which measures whether the experimental designs... | Rebuttal 1:
Rebuttal: Many thanks for your detailed and comprehensive review. We're pleased you found our evaluation criterion reasonable and the literature topic important. As shown below, we address your main concerns one by one:
---
> Q1: Unclear evaluation setup for the Equation Inference.
We apologize for the c... | null | null | null | null | null | null |
PISA Experiments: Exploring Physics Post-Training for Video Diffusion Models by Watching Stuff Drop | Accept (poster) | Summary: Current large-scale pre-trained video generation models excel in content creation but are not suitable as physically accurate world simulators out of the box. Therefore, this paper introduces the PISA framework, providing diagnostic tools for assessing the physical modeling capabilities of video generation mod... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. We are extremely grateful for the feedback given, and below we address the main concerns raised.
> **Q1:** Lack of human evaluation.
This is a great point. While our work focused on quantitative evaluation, human evaluation is import... | Summary: The paper argues that the large-scale video generative modeling has enabled creative content generation but the accurate world modeling is missing. This is due to the complexities in the physical laws and perspectives that underpin the real-world videos. To solve this problem, the paper proposes to use targete... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. We are extremely grateful for the feedback given, and below we address the main concerns raised.
> **Q1:** Lack of human evaluation.
This is a great point. While our work focused on quantitative evaluation, human evaluation is import... | Summary: **Main Findings:**
- This paper addresses the physics-based task of modeling object freefall in video diffusion models, specifically formulated as follows: given an initial image of an object suspended midair, the goal is to generate a video depicting the object realistically falling, colliding with the groun... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. We are extremely grateful for the feedback given, and below we address the main concerns raised.
> **Q1:** Limited scope of the paper.
Our goal in this paper is not to create a generalist state-of-the-art physics video model, but rat... | Summary: This work finds the generations of SOTA video generation models are visually impressive but are physically inaccurate. This work rigorously examines the post-training process of video generation models by focusing on the simple yet fundamental physics task of modeling object freefall which is highly challengin... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to review our work. We are extremely grateful for the feedback given, and below we address the main concerns raised.
> **Q1:** Marginal improvement from ORO.
On simulated data, ORO yields substantial gains. As the goal of our work is to study the proces... | null | null | null | null | null | null |
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage | Accept (poster) | Summary: This paper studies how to evaluate and tackle the hallucination phenomenon of MLLM. It first conduct a motivation experiments and conclude that existing hallucination detection methods struggles with long captions. Then it proposes a new multi agent approach which involves a LLM to decompose the original long ... | Rebuttal 1:
Rebuttal: **We are pleased to share that, in support of open-source research, we have decided to release our carefully curated VQA dataset and evaluation codes. This dataset includes 1k images, each paired with approximately 36 question-answer sets.**
We sincerely thank you for your thorough review. We hav... | Summary: This paper looks at preventing hallucination in long-form image captions, proposing a system "CapMAS" which decomposes generated captions into atomic statements, which are then generated/corrected using a VLM. The paper also introduces two metrics for image caption evaluation based on a similar pipeline: Factu... | Rebuttal 1:
Rebuttal: **We are pleased to share that, in support of open-source research, we have decided to release our carefully curated VQA dataset and evaluation codes. This dataset includes 1k images, each paired with approximately 36 question-answer sets.**
We sincerely thank you for your thorough review. We hav... | Summary: This paper focuses on generating long, detailed captions for images. A key idea in the paper is to decompose long captions into atomic claims using an LLM, and then verify every claim individually in the context of the image using a VLM. The paper motivates this by showing that this approach outperforms altern... | Rebuttal 1:
Rebuttal: **We are pleased to share that, in support of open-source research, we have decided to release our carefully curated VQA dataset and evaluation codes. This dataset includes 1k images, each paired with approximately 36 question-answer sets.**
We sincerely thank you for your thorough review. We hav... | Summary: This paper proposes a multiagent approach that leverages LLM and MLLM to correct given captions and designs two metrics for evaluating generated caption. A dataset is collected for one of the metrics.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: - In Table 4, after the captions were corrected, ... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our method’s potential to improve the factual accuracy of image captions and our novel approach leveraging MLLM–LLM collaboration without training a separate corrector. We also thank you for your suggestions, which have enrich... | null | null | null | null | null | null |
Categorical Schrödinger Bridge Matching | Accept (poster) | Summary: The authors:
- provide a proof for the convergence of discrete-time IMF in discrete-state spaces.
- develop an algorithm called "Categorical SBM" that approximates a solution to the SB problem for discrete-state spaces.
Claims And Evidence: I'm not well equipped to answer this question.
Methods And Evaluatio... | Rebuttal 1:
Rebuttal: Thank you for your comments and positive evaluation. We will correct the typos you mentioned. If you're interested in the topic, we recommend checking the other reviews and these works on the Schrödinger Bridge Problem [1, 2, 3].
[1] Kim, Jun Hyeong, et al. "Discrete Diffusion Schrödinger Bridge ... | Summary: The paper proposes an algorithm based on Iterative Markovian Fitting (IMF) for solving Schrödinger Bridge (SB) in discrete (categorical) space. The contribution of the paper therefore lies in the extension of SB, originally constructed in continuous state spaces, and its data-driven learning-based algorithm to... | Rebuttal 1:
Rebuttal: Dear reviewer 1AGf thank you for your questions and commentaries.
**[Q. 1] Can the author clarify the difference between proposed method to DDSBM, which does present theoretical results for continuous-time IMF in discrete spaces? I'm fairly familiar with DDSBM and, since both are based on IMF, t... | Summary: The paper addresses the Schrodinger Bridge (SB) problem for discrete spaces (categorical data). It proposes CSBM: a method that extends IMF (actually D-IMF) to discrete categorical spaces, proving theoretical convergence, propoing a concrete implementation and showing experimental evidence with two practical r... | Rebuttal 1:
Rebuttal: Dear reviewer atNH thank you for your questions and commentaries.
**[R. 1] Hoogeboom et al., and Gat et al, are mentioned but could use further discussion and perhaps comparison**
Regarding the references [1, 2] you mentioned, we believe they are not suitable for comparison in our setting. For [... | Summary: This paper introduces Categorical Schrödinger Bridge Matching, an approach that extends the Schrödinger Bridge (SB) framework to discrete spaces. While SB has gained traction in generative modeling and domain translation, most prior work has been confined to continuous spaces. The paper addresses this gap by d... | Rebuttal 1:
Rebuttal: Dear reviewer MBG2, thank you for raising important questions regarding our paper.
**[W. 1] Questionable Novelty of the Theoretical Contribution**
We respectfully disagree. First, as highlighted in Table 1, the continuous-time frameworks DDSBM [1] and DSBM [2] rely heavily on the theoretical fo... | Summary: The paper does what the title says: it establishes the basic framework for the version of Schrodinger bridge diffusion models, for the case of discrete (categorical) spaces. This means that it has a theoretical result describing why and how an iterated projection method for finite-steps markov processes can be... | Rebuttal 1:
Rebuttal: Dear Reviewer McJt, thank you for pointing out the unclear parts you encountered. We will revise the text where you have highlighted, as much as possible.
**[R.1] Line 110 column 2: "additional properties" sounds too vague, maybe state some examples**
In line 110, by "additional properties", we... | null | null | null | null |
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces | Accept (poster) | Summary: This paper demonstrates that alignment between the individual components of task-specific and merged matrices is strongly correlated with performance improvements over a pre-trained model. Building on this finding, the authors propose an isotropic merging framework that flattens the singular value spectrum of ... | Rebuttal 1:
Rebuttal: We are pleased that the Reviewer appreciates the soundness of our introduced metrics, the simplicity and effectiveness of the proposed approaches, and clear writing. We thank the Reviewer for the comments and we respond below to specific points.
>[Reference 1 (R1)]: *Section 4.2, which discusses ... | Summary: This paper proposes a novel model merging framework that enhances alignment between subspace of task models and merged model. The framwork includes two algorithms, (1) Iso-C that achives isotropic by flattenning the spectrum to the averaged singular values and (2) Iso-CTS in which lowest spectral components ar... | Rebuttal 1:
Rebuttal: We are glad that the Reviewer appreciates the soundness of the experimental protocol and analyses, the effectiveness of the proposed approach, and clear writing. We thank the Reviewer for their comments and we respond below.
> [Weakness 1 (W1)]: *Lacks theoretical justification...*
We provide a ... | Summary: This paper focuses on bridging the performance gap between the merged and task-specific models. They first show that the subspace alignment of merged and task-specific models correlates with performance improvement. Then, they propose an isotropic merging method to improve the merging performance via flattenin... | Rebuttal 1:
Rebuttal: We are pleased that the Reviewer appreciates the novelty of the proposed method, the significance of our contribution, and the clear writing. We thank the Reviewer for their constructive feedback, and below we respond to specific points raised.
> [Question 1 (Q1)]: *In Fig.2, the author tries to ... | Summary: The paper studies how to improve model merging methods by leveraging the singular value decomposition (SVD) of task matrices, defined as the differences between fine-tuned models' weight matrices and the pre-trained model. The authors first show that merging performance correlates with the alignment between th... | Rebuttal 1:
Rebuttal: We are pleased that the Reviewer acknowledges the novelty of the proposed method, the contribution towards understanding model merging and clear writing. We thank the Reviewer for the constructive feedback, and below we respond to specific points raised.
> [Minor weakness 1 (MW1)]: *Sec. 3, along... | null | null | null | null | null | null |
EpiCoder: Encompassing Diversity and Complexity in Code Generation | Accept (poster) | Summary: The paper introduces *EpiCoder*, a novel approach to enhancing code-generation performance through hierarchical feature trees extracted from seed code. Empirical results demonstrate that EpiCoder surpasses similarly sized baselines in functional correctness (measured via pass@k) and complexity (measured using ... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and valuable feedback on our paper.
## 1. Additional References
We appreciate your suggestion and will include discussions on them in our paper. Li et al. (2024) explore the use of LLMs for rewriting code to enhance code search performance, while Koziolek et al.... | Summary: This paper presents EpiCoder, a novel framework designed for code generation, addressing the limitations of existing methods that rely on code snippets as seed data. It introduces a feature tree-based synthesis approach that captures hierarchical code features, enhancing complexity and diversity in generated ... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We address your concerns below and hope these clarifications help resolve them.
## 1. Missing Supplementary Material
Our paper includes a 26-page appendix at the end, which provides extensive details on our methodology, implementation, and experiments.
## ... | Summary: This paper introduces EpiCoder, a feature tree-based framework for code generation that addresses diversity and complexity in generated code. The authors propose a hierarchical feature to represent features like concepts used in the code. The framework consists of three components: (1) Feature Tree Extraction,... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for recognizing the novelty and comprehensiveness of our work. We address your concerns below.
## 1. Train/Test Leakage in Function-Level Benchmarks
We address train/test leakage analysis in Appendix B.2 (Figure 9), demonstrates that EpiCoder has a low... | Summary: This paper presents a new data synthesis method to generate complex and diverse code data. Given some seed code data, this method prompts an LLM to extract code features (e.g., functionality concepts, programming paradigm, etc.) from each code and organize them into a tree structure (i.e., feature tree). It th... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and for recognizing the contribution of our work. Below, we address your key concerns.
The table link [https://anonymous.4open.science/r/epicoder_rebuttal-C619/tables.md](https://anonymous.4open.science/r/epicoder_rebuttal-C619/tables.md) contains all the table... | null | null | null | null | null | null |
Advancing Constrained Monotonic Neural Networks: Achieving Universal Approximation Beyond Bounded Activations | Accept (poster) | Summary: Authores generalize previous theoretical results, showing that MLPs with non-negative weight constraint and activations that saturate on alternating sides are universal approximators for monotonic functions. Additionally, they show an equivalence between saturation side in the activations and sign of the weigh... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you very much for your positive feedback. We appreciate your suggestion and will clarify the definitions of the terms you've highlighted, in our revised manuscript.
If you have any additional recommendations or improvements you'd like to see to elevate your rating further, p... | Summary: This paper extends the body of work on monotonic neural networks.
It focuses on relaxing the existing constraints that limits the architecture, use, and performance of such networks.
Specifically, this work identifies limitations within the existing architecture including the use of threshold neurons, which ... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your constructive review and valuable comments. Below we address each of your concerns in detail:
- _Typos & Figure 1_:
Thank you for highlighting these issues. We have thoroughly revised the manuscript, corrected all identified typos, and will explicitly cite Figu... | Summary: This paper proposes a novel Monotonic Neural Network as a universal approximator for monotone functions. Unlike previous works, this approach provides theoretical proof that the proposed Monotonic Neural Networks can serve as universal approximators and successfully removes the constraint of activation functio... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your insightful review and constructive suggestions. Below, we address each of your points individually:
- _"Emphasizing the relevance and potential applications of Monotone MLPs would significantly enhance the paper’s impact."_:
We fully agree with your suggesti... | Summary: This paper constructs universal approximators for monotonic functions with MLPs with non-negative weight constraint and activations that saturate on alternating sides. Based on the result, the paper shows MLPs with convex monotone activations and non-positive constrained weights can also be universal approxima... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your thoughtful comments and suggestions. Below, we address each of your points in detail:
- _"The two formulations are not equivalent to the original monotonic ReLU MLP."_:
It is unclear whether the "original monotonic ReLU MLP" refers specifically to the sett... | null | null | null | null | null | null |
Zero-Shot Generalization of GNNs over Distinct Attribute Domains | Accept (poster) | Summary: The authors study the problem of generalizing GNNs to new graphs that have distinct node/edge attributes. This is a very important problem when attempting to create graph foundation models, as different graphs will often have very different attributes. These attributes will differ not only in dimension size, b... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for recognizing that our paper “should hold great interest to many in the field of Graph ML” and that STAGE can help alleviate the significant “impediment to the creation of graph foundation models.” We now address their remarks.
**Q1.** “There is still overlap in the s... | Summary: The paper introduces STAGE, a novel framework designed to overcome the challenge that traditional GNNs face when node attributes in test graphs differ from those seen during training. Rather than relying on raw attribute values, STAGE computes statistical dependencies between pairs of attributes by constructin... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for recognizing our theory is “rigorous and well-founded.” We now address their comments.
**Q1.** “The approach is quadratic in complexity concerning the number of attributes, which may limit its applicability to very large graphs or datasets with high-dimensional attri... | Summary: This paper studies the zero-shot generalization of GNNs under the shift in attribute domains. They propose the STAGE algorithm that aims to model the statistical dependencies between node attributes that can be invariant across domains instead of the original node attribute. Specifically, STAGE creates the edg... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition that STAGE “can be useful in many real-world adaptation scenarios,” and thank them for acknowledging STAGE’s versatility, as well as for their appreciation of our theoretical motivation and experiments. We now address their comments.
**Q1:** “..It might st... | Summary: The paper introduces STAGE (Statistical Transfer for Attributed Graph Embeddings) to enhance the zero-shot generalization capabilities of Graph Neural Networks (GNNs). It represents node attributes using order statistics, treating node features as random variables and reconstructing them into a STAGE-edge-grap... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s recognition of STAGE as *“marking a significant step towards foundational graph models”* and their positive assessment of our theory and empirical results. We will incorporate their suggestions into the revised manuscript. Below, we address their remarks:
**Q1.** Buil... | null | null | null | null | null | null |
Distributed Conformal Prediction via Message Passing | Accept (poster) | Summary: This work studies CP in a decentralised inference setting, where multiple devices share the same pre-trained model, and each device has a local calibration data set (motivated by e.g. privacy constraints). Given a common input, the devices aim at producing a prediction set that includes the true label of the t... | Rebuttal 1:
Rebuttal: First, we are happy to consider your valuable suggestions in **Other Strengths And Weaknesses** and **Other Comments Or Suggestions**. For the other questions, please find the point-to-point response below.
1. > Do you require the calibration data to be iid across devices?
Please see our repl... | Summary: This paper proposes a distributed way of achieving conformal prediction intervals. It extends the current literature by looking at graphs other than a star graph, and by looking at histogram summaries in addition to quantiles.
"## update after rebuttal: I have changed my recommendation to accept.
Claims And ... | Rebuttal 1:
Rebuttal: 1. > The benchmarking seems ok; a standard Cifar data set is used. It could be interesting to also look at synthetic examples.
Please refer to the rebuttal of Reviewer 6Gbu for details on experiments on a different data set.
2. > It is not clear how one can validate Assumption 4.1, that is, h... | Summary: The paper introduces two novel algorithms for distributed conformal prediction in decentralized networks. The Q-DCP employs ADMM to solve a distributed quantile regression problem with a smoothed pinball loss $ \tilde{\rho}\_\gamma(s)$ (incorporating a smoothing function $ \tilde{g}(x)$ and regularization term... | Rebuttal 1:
Rebuttal: 1. > The largest network tested has only 20 devices...
To validate the proposed method on a larger network, we considered a network with $100$ devices, each of which collects data from a distinct class, setting $T=3000$ for both H-DCP and Q-DCP (with $\epsilon=0.5$). The experiment results, wh... | Summary: This paper addresses the challenge of conformal prediction in decentralized settings where multiple devices have limited calibration data and can only communicate with neighboring devices over arbitrary graph topologies. The authors propose two methods for distributed conformal prediction: Quantile-based Distr... | Rebuttal 1:
Rebuttal: 1. > The experiments are limited to a single dataset (Cifar100). Including additional datasets...
Following your advice, we have evaluated the proposed scheme on a healthcare dataset, namely [PathMNIST](https://medmnist.com/). PathMNIST includes $9$ classes and 107,180 data samples in total.
... | null | null | null | null | null | null |
Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models | Accept (poster) | Summary: This paper studies the membership inference risk for local differential-privacy (LDP) protected clients in the presence of dishonest servers who can actively manipulate the model parameters. The paper provides theoretical upper and lower bounds for the success rates of low-polynomial-time membership attacks. I... | Rebuttal 1:
Rebuttal: Thank you for the positive feedback that our research is well-motivated and the provided theoretical bounds are useful for quantifying the risk of membership inference.
## #1
`Theoretical Claims: No`
We see that the reviewer indicates we do not have theoretical claims. However, we'd like to clar... | Summary: This paper examines the privacy risks posed by Active Membership Inference (AMI) attacks against
federated learning (FL) clients even when their data is protected by Local Differential Privacy (LDP). The
authors derive theoretical lower and upper bounds for the success rates of low-polynomial-time attacks
expl... | Rebuttal 1:
Rebuttal: ## #1
- `evaluation is only limited to certain types of attacks and 2 LDP mechanisms`
- `the study only considers noise added directly to the data, while other common LDP approaches are perturbing the gradients before aggregation.`
We would like to reiterate that our **theoretical** analysis app... | Summary: The paper "Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Models" investigates the vulnerability of federated learning (FL) systems, particularly those protected by Local Differential Privacy (LDP), to Active Membership Inference (AMI) attacks. The authors derive th... | Rebuttal 1:
Rebuttal: ## #1
`Have the authors considered other privacy-preserving techniques, such as secure multi-party computation (SMPC) or homomorphic encryption, and how they might impact the success rates of AMI attacks?`
We thank reviewer eoeX for their insightful comments. First, we’d like to clarify that secu... | null | null | null | null | null | null | null | null |
Sharp Optimality of Simple, Plug-in Estimation of the Fisher Information of a Smoothed Density | Accept (poster) | Summary: This paper analyzes the minimax rate for estimation of the Fisher Information of a 1-dimensional Gaussian-smoothed density that satisfy an alpha-Holder condition from samples. It shows that variants of the simple plug-in estimator achieves the minimax rate, which varies depending on the amount of Gaussian smoo... | Rebuttal 1:
Rebuttal: Thank you for your helpful review!
__Essential references not discussed:__
Thanks for pointing these very relevant papers out! We plan to add the following text (perhaps with some modification to obey space constraints) to the revised manuscript.
"The smoothed Fisher information has also been re... | Summary: This paper studies the problem of estimating the Fisher information of smoothed probability densities falling in the $\alpha$-Holder smooth class. The authors derive minimax rate bounds for the plug-in estimator, showing that a simple plug-in estimator is optimal for smoothed probability densities. The converg... | Rebuttal 1:
Rebuttal: Thanks for the thoughtful report!
__Other suggestions 1:__ Reviewers pEyQ and arrG also asked about computation, which is related to your comment. Let us first describe how computation of the estimators $\widehat{\mathcal{I}}\_t$ can be done, since it involves an integral over an infinite interva... | Summary: This paper studies estimation of the Fisher information $\mathcal{I}(f * \psi_t)$ of a smoothed density $\psi_t$, where $\psi_t$ is the Gaussian kernel of bandwidth $t$, given IID samples from a density $f$. Plug-in estimators are proposed, based on appropriately truncated and smoothed estimates of $f * \psi_t... | Rebuttal 1:
Rebuttal: Thanks for the great feedback!
__Questions for Authors 1:__ Reviewer pEyQ asked the same question; please see our response. Thanks!
__Questions for Authors 2:__ Thank you for the question, and we agree this point could have, and should have, been made clearer in the paper.
As you point out, we ... | Summary: The paper considers probability densities smoothed by Gaussian noise of variance $t$, and addresses the problem of estimating the Fisher information of the smoothed densities based on a collection of $n$ i.i.d. samples. The Fisher information can be expressed as an integral of the smoothed density and its deri... | Rebuttal 1:
Rebuttal: Thanks for the constructive comments!
__Weakness 1:__
Suppose $f$ is an $\alpha$-Holder density on $[-1, 1]^d$. Our result can be directly extended, and the rate is entirely expected:
$$\inf\_{\widehat{\mathcal{I}}\_t} \sup_{f \in \mathcal{F}\_\alpha} E\left(\left|\widehat{\mathcal{I}}\_t - \mat... | null | null | null | null | null | null |
Generalizable Multi-Camera 3D Object Detection from a Single Source via Fourier Cross-View Learning | Accept (poster) | Summary: This paper proposed a Fourier Cross-View Learning (FCVL) framework, which augments the data in the frequency domain and includes a contrastive-style semantic consistency loss to improve the model generalization ability from a single source.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Yes.
Th... | Rebuttal 1:
Rebuttal: Thanks for your positive and constructive feedback! We have addressed all the comments and incorporated additional experimental results to further validate our approach.
__Q1 and W1:__ In our approach, there is a cross-view instance binding mechanism that the identical instance labels are assig... | Summary: The author proposes the Fourier Cross-View Learning (FCVL) framework including Fourier Hierarchical Augmentation (FHiAug),
an augmentation strategy in the frequency domain to boost domain diversity, and Fourier Cross-View
Semantic Consistency Loss to facilitate the model to learn more domain-invariant features... | Rebuttal 1:
Rebuttal: Thanks for your acknowledgment of our approach, which is truly encouraging! We have addressed all the comments and incorporated additional experimental results to further validate our approach. We sincerely appreciate your contributions to help elevate the quality of this submission. __All the tab... | Summary: The authors propose a novel generalization multi-camera 3D object detection framework using Fourier Cross-View Learning.
Via the proposed Fourier Hierarchical Augemetatiion and Semantic Consistency Loss across views, this work consistently improves the generalization ability of the previous methods over multip... | Rebuttal 1:
Rebuttal: We are pleased that the reviewer found our paper __novel, interesting and effective__. Thanks very much for your acknowledgment, which is truly encouraging! We have addressed all the comments and further improved the manuscript. We are deeply grateful for your contributions to help elevate the qua... | Summary: Aiming to improve the generalization in only single source data available for training, this paper proposed Fourier Cross-View Learning (FCVL) framework. FCVL framework can leverage the Fourier transformation to separate high-level and low-level information within the image. Subsequently, it can make appropria... | Rebuttal 1:
Rebuttal: Thanks for your positive and constructive feedback! We have addressed all the comments and incorporated additional experimental results to further validate our approach.
__Q1: In this paper, use the nuScenes dataset as the training set and the NuScenes-C dataset as the testing sets. Why don't us... | null | null | null | null | null | null |
Bongard in Wonderland: Visual Puzzles that Still Make AI Go Mad? | Accept (poster) | Summary: This paper presents a case study of utilizing VLMs for solving Bongard problems, and identifies that it remains challenging for VLMs to reason some basic concepts in Bongard problems. The authors also conduct a comparison between VLMs' and human's reasoning abilities on Bongard problems.
Claims And Evidence: ... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our work and for considering it well written. We hope that the following responses will also convince you of the strength and value of its contributions.
**(W1, Q1 - No new method)**
We respectfully disagree with the reviewer’s assessment regarding the la... | Summary: The paper benchmarks existing vision-language models using Bongard problems (BPs). It also performs a human evaluation for comparison. The paper tests not only whether a model can solve a given BP or not, but also whether the main concept in the BP can be recognized in the individual images in the BP, and whet... | Rebuttal 1:
Rebuttal: Thanks for your detailed response and the constructive feedback! Below we address your concerns.
**(W1 - Human study for Task 2)**
We agree that analyzing human performance in Task 1 alongside Task 2 would be an interesting future direction in a different setting with higher conceptual ambig... | Summary: This paper explores the performance of VLMs on Bongard problems. To test the abstract reasoning ability of VLMs, three different types of tasks are proposed: (1) open-ended solving of Bongard problems, (2) detection of specific concepts, and (3) formulation of hypotheses. Task 1 is to summarize the rules of th... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We respond to your points in detail below.
**(W1 - Selection of test data)**
We chose to work with the original Bongard problems introduced in [1], as they were specifically designed to test pattern recognition capabilities in machines, yet they remain unsol... | Summary: The paper evaluates current VLMs on Bongard Problems. Each Bongard Problem (BP) consists of 12 images divided into two sides — the left side and the right side — each side containing 6 images. The images on each side are characterized by a rule not shared by the other side. The aim of the problem solver is to ... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback and questions, we address them below.
**(W1 - Differences to Malkinski et al.)**
Malkinski et al. (2024) also evaluated VLMs on Bongard problems, concentrating on open-ended and classification-based settings. Our work shares their open-ended focus but goes fu... | null | null | null | null | null | null |
Adversarial Inputs for Linear Algebra Backends | Accept (poster) | Summary: The authors propose a white-box attack to construct "Chimera examples", or inputs to models that elicit conflicting predictions depending on the employed backend library, and proposed a PRNG-based defense against it.
## update after rebuttal
The rebuttal addresses most of my concerns. In particular, I'm happ... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper!
**Experiments with larger models.** We have extended our evaluation to include the ImageNet dataset, using the more complex architectures ResNet18 (Top-1 Accuracy: 69.7%) and EfficientNetV2S (Top-1 Accuracy: 84.2%). For these experiments, we used a reduce... | Summary: This paper investigates the vulnerability in neural network inference caused by minor discrepancies in linear algebra backends used by popular frameworks like TensorFlow and PyTorch. The authors introduce "Chimera examples," which are specially crafted inputs that produce conflicting predictions depending on t... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper!
**Experiments with larger models.** We have extended our evaluation to include the ImageNet dataset, using the more complex architectures ResNet18 (Top-1 Accuracy: 69.7%) and EfficientNetV2S (Top-1 Accuracy: 84.2%). For these experiments, we used a reduc... | Summary: The paper presents a method that exploits differences in the numerical computation implementations of linear algebra backends that power the major ML frameworks to construct adversarial examples.
Claims And Evidence: Strengths:
- In the space of constructing adversarial examples, this paper is very novel and ... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper!
**Experiments with larger models.** We have extended our evaluation to include the ImageNet dataset, using the more complex architectures ResNet18 (Top-1 Accuracy: 69.7%) and EfficientNetV2S (Top-1 Accuracy: 84.2%). For these experiments, we used a reduc... | Summary: This paper claims that the implementations of linear algebra used by popular frameworks such as PyTorch and TensorFlow are not exactly consistent. The difference between these implementations can be quantified using a term called ULP (Unit in the Last Place). The authors demonstrate that this small gap is enou... | Rebuttal 1:
Rebuttal: Thank you for your feedback on our paper!
**Chimera vs. adversarial examples.** We are sorry that a key distinction between Chimera examples and adversarial examples did not come across clearly. Constructing a Chimera example always requires considering at least two backends simultaneously. If o... | null | null | null | null | null | null |
Editable Concept Bottleneck Models | Accept (poster) | Summary: Editable CBMs provide the ability to edit a trained CBM to account for issues in annotation errors, concept set changes, and problems with specific data points. That is done with the help of influence functions that approximate the model.
Claims And Evidence: The claims made are largely clear and supported by... | Rebuttal 1:
Rebuttal: -*Response to Claims And Evidence*
We respectfully disagree with your opinion. The primary goal of CBM is to explicitly decompose the model's intermediate representation into a set of interpretable concepts, typically predefined by domain experts or specific task requirements before training. How... | Summary: The paper introduces Editable Concept Bottleneck Models (ECBMs), an extension of Concept Bottleneck Models (CBMs) that allows efficient data and concept removal without full retraining. Using influence functions and Hessian-based approximations, ECBMs support three levels of editability: concept-label, concept... | Rebuttal 1:
Rebuttal: -*Response to Weaknesses 1*
Thanks for your invaluable advice. We will add this part in the revision. Here, we provide the analysis for algorithm 1.
The time complexity of the algorithm is \( O(n \cdot (m^2 + d^2) + s_e \cdot m^2 + d^3) \), where \( n \) is the number of data points, \( m \) is ... | Summary: The authors present Editable CBMs, where they consider _editability_ from the lens of retraining CBMs at three different levels: 1) Concept Label-level, i.e. when there's label noise in the concept space, 2) Concept level, i.e. removing spurious concepts from the bottleneck predictions and 3) Data-level, i.e. ... | Rebuttal 1:
Rebuttal: ## Weakness:
-*Response to W2: the authors have only considered sequential setting (probably the joint setting as it gives the best performance)*
We sincerely thank the reviewer for highlighting the importance of the jointly training mode in CBM. We agree that joint training sometimes leads to h... | Summary: This paper improves Concept Bottleneck Models (CBMs) by proposing how to update or “edit” a well-trained CBM. The issues arise when the concept-label level annotations need to be updated, concepts themselves need to be removed and certain data samples used in the training of the model themselves need to be rem... | Rebuttal 1:
Rebuttal: -*Response to W2: the authors have only considered sequential setting (probably the joint setting as it gives the best performance)*
We sincerely thank the reviewer for highlighting the importance of the jointly training mode in CBM. We agree that joint training sometimes leads to higher accuracy... | null | null | null | null | null | null |
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction | Accept (oral) | Summary: This paper argues that the test MAE, when energy conservation is guaranteed in MD simulations, demonstrates the practicality of machine learning potentials. The authors provide empirical evidence indicating that, within established model designs, specific designs uphold energy conservation principles while oth... | Rebuttal 1:
Rebuttal: We thank reviewer yR9i for the helpful feedback. We address each of the reviewer’s comments below.
> It should be noted somewhere in the text that the physical properties considered in this study are limited to those requiring higher-order derivatives of the PES and that the applications of machi... | Summary: This paper draws attention to the inability of energy conservation, and thereby instability of simulation, common in many popular machine learning interatomic potentials (MLIPs). Next, it proposes a novel architecture addressign this problem, while showing state-of-the-art performance on a wide range of tasks.... | Rebuttal 1:
Rebuttal: We thank reviewer 8LdR for the helpful feedback. We address each of the reviewer’s comments below.
> Can the claims made in Section 5 be supported by theoretical arguments?
We refer to (1) Hairer et al. 2003 for theoretical arguments on the relationship between potential energy surface (PES) smo... | Summary: This work investigates failure cases of machine learning interatomic potentials (MLIPs) in actual MD simulations. From these insights, the authors draw actionable improvements to MLIP that they implement in their eSEN model. eSEN shows promise in being more accurate on hold-out test sets as well as in preservi... | Rebuttal 1:
Rebuttal: We thank reviewer kXc9 for the helpful feedback. We address each of the reviewer’s comments below.
> Its main downside is the limited originality in its technical and theoretical contribution.
Regarding the originality of our technical and theoretical contributions, we would like to highlight t... | Summary: This paper presents eSEN, a machine learning interatomic potential (MLIP) model designed for accurate and energy-conserving molecular dynamics (MD) simulations and physical property predictions. The study identifies key factors that impact an MLIP’s ability to generalize well to physical property prediction ta... | Rebuttal 1:
Rebuttal: We thank reviewer XYRs for the helpful feedback. We address each of the reviewer’s comments below.
> The design choices of the eSEN model are all from existing works.
Regarding the originality of our technical contributions, we would like to highlight that while energy-conservation-related desig... | null | null | null | null | null | null |
RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer | Accept (poster) | Summary: The paper analyzes an important problem of modality biases in multimodal learning setup. The authors find that the dynamic property of attention is lost during multimodal training; that is, rather than weighing the modalities per-instance, the models just focus on a single (biased) modality, which is overempha... | Rebuttal 1:
Rebuttal: Dear reviewer gn7r,
**Thanks a lot for your valuable review, suggestions, and questions.**
**Q1: Extension to more fusion paradigms**
> Q1.a: Analysis of cooperation dynamics.
To analyze cooperation dynamics across fusion methods, we monitor the gradient of unimodal encoders and the attentio... | Summary: This paper focuses on fusion strategies in multimodal transformers, identifies issues in dynamic fusion, proposes the QRR algorithm, and validates its effectiveness in restoring cooperation dynamics and improving performance through experiments
Claims And Evidence: The paper's claims are supported by some evi... | Rebuttal 1:
Rebuttal: Dear reviewer FKSM,
**We appreciate your time and great efforts in reviewing.**
We carefully considered your comments on the validation of assumptions, lack of in-depth analysis and extension to benchmarks and methods and conducted corresponding experiments and theoretic analysis.
**Q1: The va... | Summary: The paper identifies the issue of the self-reinforcing cycle toward the majority modality in multimodal learning. To address this, the authors propose a query rebalance rotation method that disrupts the cycle and rebalances the attention mechanism. Experimental results and visualizations demonstrate the effect... | Rebuttal 1:
Rebuttal: Dear reviewer 61wP,
**Thank you very much for your affirmation and constructive comments.**
We carefully considered your comments and conducted corresponding experiments.
**Q1: The change of gradients during training.**
Thank you for your question. From the optimization perspective, previous ... | null | null | null | null | null | null | null | null |
GEFA: A General Feature Attribution Framework Using Proxy Gradient Estimation | Accept (poster) | Summary: This paper introduces GEFA, a feature attribution framework leveraging proxy gradients to generate explanations for different kinds of ML models. Unlike prior gradient-based explainers that operate under white-box assumptions, GEFA is designed to work for black-box models, and it is applicable to models with o... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the detailed comments and the efforts devoted to reviewing the paper. We are encouraged that our efforts in providing theoretical grounding for the proposed approach were well perceived. Our point-to-point responses to the concerns and questions raised in t... | Summary: This work presents GEFA -- Gradient-estimation-based Explanation For All. GEFA is a general feature attribution framework based on proxy gradient estimation. The authors argue that GEFA offers a black-box explainability solution that is broadly applicable across different input modalities (e.g., images, text)... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the detailed comments and the efforts devoted to reviewing the paper. We are encouraged that our theoretical grounding for the proposed approach was well received and that the reviewer liked the analyses. Our point-to-point responses to the concerns and que... | Summary: In this work, the authors propose a blackbox feature attribution method based on proxy gradient estimation. Specifically, they introduce proxy variables, each denoting a binary feature-level selection. The authors show that their approach is an unbiased estimator of shapley values, thus sharing some of the nic... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the detailed comments and the efforts devoted to reviewing the paper. Our point-to-point responses to the concerns and questions are given below.
**Test setting for images**: We consider two image classifiers — InceptionV3 and ViT — as shown in Table 2. Th... | Summary: In this paper, the authors propose a new method for input attribution in DNNs. They focus on the attribution in black-box models, where the gradient is unavailable. In this case, they propose the proxy gradient space for estimation, and then define the attribution of input features. The authors further prove t... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for the detailed comments and the efforts devoted to reviewing the paper. Our point-to-point responses to the concerns and questions are given below.
We start with the relationship between GEFA and SHAP:
1. Both GEFA and SHAP are unbiased estimators of Shapley... | null | null | null | null | null | null |
Set Valued Predictions For Robust Domain Generalization | Accept (poster) | Summary: The paper introduces a set-valued prediction approach for robust Domain Generalization (DG). It argues that single-valued predictions limit robustness, proposing instead to predict sets of labels to achieve reliable coverage across unseen domains. The authors provide theoretical generalization bounds and intro... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We have gained many important insights from your questions, and appreciate the opportunity to address your concerns.
1. SET-COVER incurs a higher training time (~30% increase over ERM) due to the additional optimization of Lagrangian multipliers (d... | Summary: This paper introduces a set-valued predictor approach for domain generalization (DG) to address the limitations of single-valued predictions in unseen domains. The authors argue that set-valued outputs can capture diverse feature-label relationships across domains, enhancing robustness. They present a theoreti... | Rebuttal 1:
Rebuttal: Thank you very much for your valuable feedback. We appreciate your insights and are happy to address your concerns.
1. SET-COVER incurs a higher training time (~30% increase over ERM) due to the additional optimization of Lagrangian multipliers (denoted as C in our algorithm). Below are the avera... | Summary: This paper proposed set valued predictions for domain generalization, with theories and experimental justifications. This work builds upon some theoretical basis on uniform convergence considering domains and the conditions of uniform convergence based on the finite VC-dimension. The paper further prove the ac... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. Your questions and comments are very valuable and we appreciate the opportunity to clarify the key points raised.
1. Our theoretical results address whether achieving a target performance level (e.g., passing a recall threshold level) on training d... | Summary: This paper introduces set-valued predictions for domain generalization (DG) problems. They propose a framework based on counting threshold violations for per-label recall. The paper introduces SET-COVER (SET Coverage Optimized with Empirical Robustness), a relaxed (differentiable) version of the proposed metri... | Rebuttal 1:
Rebuttal: Thank you very much for your constructive review. We have gained many important insights from your questions, and believe we can address your concerns. Below, we have organized our response by the key topics raised in your review:
1. Our primary focus was on comparing SET-COVER with other set-val... | null | null | null | null | null | null |
Synthetic Face Datasets Generation via Latent Space Exploration from Brownian Identity Diffusion | Accept (poster) | Summary: The authors propose an approach to generate synthetic face images, by leveraging a GAN-based backbone, coupled with novel Langevin and Dispersion algorithms, together used as DisCo, wherein both inter-class and intra-class diversity in ensured by using a physics informed formulation.
Claims And Evidence: Whil... | Summary: n this paper, the authors introduce a physics-inspired method to generate large synthetic face datasets for training face-recognition models. Their core idea is to treat each latent representation as a “particle” and let these particles repel each other in the embedding space (via a “Brownian identity diffusio... | Summary: In this work, they introduce a new method, inspired by the physical motion of soft particles subjected to stochastic Brownian forces, allowing us to sample identities distributions in a latent space under various constraints. They also introduce three complementary algorithms, called Langevin, Dispersion, and ... | null | null | null | null | null | null | null | null | |||
Scalable Attribute-Missing Graph Clustering via Neighborhood Differentiation | Accept (poster) | Summary: This paper presents a novel approach for deep graph clustering (DGC) in the presence of missing node attributes and large-scale graph structures, termed Complementary Multi-View Neighborhood Differentiation (CMV-ND). CMV-ND achieves this by pre-processing graph structural information into multiple views in a n... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 69Ph
We thank the reviewer for the careful reading and constructive feedback. Below, we address each concern in detail.
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**W1:** *The experimental results omit comparisons with some recent state-of-the-art methods in Deep Graph Clustering (DGC). It would be beneficial... | Summary: This paper proposes a method called Complementary Multi-View Neighborhood Differentiation (CMV-ND) to address deep graph clustering (DGC) on large-scale graphs with missing node attributes. CMV-ND captures multi-hop local structures using a Recursive Neighborhood Search (RNS) and eliminates redundancy with a N... | Rebuttal 1:
Rebuttal: ## Response to Reviewer WHi9
We thank the reviewer for the thoughtful comments and helpful suggestions. Below, we address each point in detail.
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**W1:** *By treating graph data as two views—attribute view and structural view—it is natural to frame the graph clustering problem as a multi-view... | Summary: The paper addresses the challenge of clustering nodes in large-scale graphs that often suffer from missing attributes, a common scenario in real-world applications such as social networks and recommendation systems. To tackle this, the authors propose the Complementary Multi-View Neighborhood Differentiation (... | Rebuttal 1:
Rebuttal: ## Response to Reviewer 7mCA
We thank the reviewer for the thoughtful comments and constructive suggestions. Below, we address each concern raised.
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**W1:** *Lack of comparison with SAT, ITR, and SVGA.*
We have conducted additional experiments comparing CMV-ND with three representative meth... | Summary: This paper presents a deep clustering method, namely Complementary Multi-View Neighborhood Differentiation (CMV-ND), to conduct clustering tasks in large-scale and attribute-missing graphs. CMV-ND adopts the Recursive Neighborhood Search to capture the complete local structure and the Neighborhood Differential... | Rebuttal 1:
Rebuttal: ## Response to Reviewer CDeb
We thank the reviewer for the careful reading and valuable feedback. Below, we address each concern raised.
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**W1:** *Lack of discussion on structure learning/search methods.*
We have considered structure learning and structure search methods, such as SUBLIME (W... | null | null | null | null | null | null |
Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation | Accept (poster) | Summary: This paper introduces ReCAP, a novel TTA method based on local inconsistency of predictions.
Based on the finding that the local inconsistency increases and adaptation becomes difficult under wild distribution shifts, the region confidence is proposed as an alternative to entropy, a common objective in TTA.
It... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper and providing valuable feedback. We would like to answer your questions below.
>Q1: Experimenting on continual TTA settings performed in recent TTA studies (e.g., EATA) would strengthen the efficacy of ReCAP in wild TTA settings.
A1: Thank you for... | Summary: This paper introduces a new Test-Time Adaptation Method (TTA) to combat domain shifts appearing at test time in extreme scenarios. In particular, it proposes ReCAP, a method that optimizes two terms: a bias term resembling a regional entropy around a given test data, and a variance term to enhance the consiste... | Rebuttal 1:
Rebuttal: We deeply appreciate your positive comments and constructive suggestions on improving our paper. We will address your questions below.
>Q1: I checked the ablation experiments and did not find the one ablating the impact of $\mathcal{L}_0$.
A1: Due to space constraints, we provide the ablation stu... | Summary: This paper proposes a new method, ReCAP, a novel approach to addressing the main limitation of TTA in entropy minimization. The key idea of this work is that EM heavily relies on local consistency, and when this consistency is disrupted, model performance degrades. To resolve this issue, instead of optimizing ... | Rebuttal 1:
Rebuttal: We appreciate your detailed review and positive feedback on our contributions, including meaningful findings, novel region-based confidence optimization, and comprehensive evaluation. Building on your comments, we provide additional explanations and experiments to further demonstrate ReCAP's effec... | Summary: This paper proposes a region modification based mechanism, called “Region Confidence Adaptive Proxy (ReCAP), to address the problem of will test-time adaptation (WTTA). Further, it develops a finite-to-infinite asymptotic approximation, which is a tractable upper bound to the intractable region confidence.
Exp... | Rebuttal 1:
Rebuttal: Thank you for carefully reviewing our paper and offering a positive assessment. We appreciate your recognition of the contributions made by our work, particularly the idea of the tractable bound on the intractable region confidence and the theoretical results.
>Q1: Some of the Empirical Gains are... | null | null | null | null | null | null |
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional | Accept (poster) | Summary: This paper presents a new method for transition path sampling in molecular systems by combining generative models with the Onsager-Machlup action functional. The authors show how pre-trained generative models (specifically denoising diffusion and flow matching) can be repurposed to find high-probability transi... | Rebuttal 1:
Rebuttal: >Comparison with enhanced sampling baselines
To our knowledge, there’s no widely accepted force field for the $\alpha$-Carbon coarse-graining used for the fast-folding proteins, so benchmarks are challenging. We now benchmark on all-atom alanine dipeptide, a standard test system for TPS. We compa... | Summary: The manuscript focuses on transition path sampling (TPS), which involves identifying high-probability paths between two states or points on an energy landscape. The authors combine generative models trained to sample temporally independent states from an energy landscape with the task of transition path sampli... | Rebuttal 1:
Rebuttal: >Comparison against baselines and incorporation of other generative models
Please see our response to reviewer bRih, in which we compare our OM optimization approach on alanine dipeptide with two traditional approaches for transition path sampling: Markov Chain Monte Carlo (MCMC) and metadynamic... | Summary: The paper proposes a way of using a score-based or a flow based generative model trained to generate molecular configurations to generate transition paths between meta-stable configurations. The paper proposes so be drawing a relation between what would be a limit SDE corresponding to noising and denoising pro... | Rebuttal 1:
Rebuttal: > Discussion of optimal diffusion time
A couple of ways that $\tau_\text{opt}$ is chosen:
1. For diffusion models, a small nonzero $\tau$ usually works better than $\tau=0$. Theorem B.1 highlights why, noting $\bar \alpha_0 = 1$. Scores closer to the data are weighted lower in a standard DDPM tr... | Summary: The paper introduces Onsager-Machlup (OM) optimization to sample transition paths, claiming three advantages: efficiency, scalability, and flexibility. OM optimization approach produce transition paths in pre-trained generative models, where the core idea is interpreting candidate paths as the denoise-noise SD... | Rebuttal 1:
Rebuttal: > Scalability and efficiency comparison to previous works
To address the concern about only including results on coarse-grained tetrapeptides, we also present OM optimization results on all-atom tetrapeptides, which contain up to 56 atoms, in [Figure 6r](https://imgur.com/a/naQLWDy). We obtain co... | null | null | null | null | null | null |
FedOne: Query-Efficient Federated Learning for Black-box Discrete Prompt Learning | Accept (poster) | Summary: The authors propose FedOne to increase the query efficiency prompt learning method for cloud-based LLM, which activates only one client per round for optimal efficiency. The proposed method is shown to be effective by extensive experiments.
Claims And Evidence: Good.
Methods And Evaluation Criteria: Fair.
T... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments and constructive criticisms. Your feedback has been invaluable in improving the quality and clarity of our manuscript. Below, we address the **Weaknesses and Essential Reference part**.
>**W1**: Motivation behind optimizing query efficiency
The... | Summary: This paper explores Federated Black-Box Discrete Prompt Learning and introduces FedOne, a novel approach that selects a single client per round. The chosen client updates the sampling probability for each token at different positions, optimizing prompt learning in a federated setting. Comprehensive experiments... | Rebuttal 1:
Rebuttal: We sincerely thank you for your insightful comments and constructive criticisms. Below, we address the weaknesses.
>**W1**: Rationale of Fed-BDPL
The rationale for employing **Black-box** Discrete Prompt Learning is grounded in two key real-world constraints:
1. **Lack of Access to Model Interna... | Summary: The paper introduces a federated learning (FL) framework designed to improve the query efficiency of Black-Box Discrete Prompt Learning (BDPL) when interacting with cloud-based Large Language Models (LLMs). Traditional federated black-box prompt tuning approaches incur high query costs due to multiple clients ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for recognizing the contribution of our work. Your feedback has been invaluable in enhancing the quality and clarity of our manuscript. We reply to the weaknesses and questions.
>**W1&Q1**: Impact of heterogeneity on theoretical analysis
Thanks for poi... | Summary: This paper introduces a federated learning framework for black-box discrete prompt learning (BDPL), specifically suitable for cloud-based LLMs. The core idea of FedOne is to optimize query efficiency by degrading the traditional FedAvg algorithm to activate only a single client per round. The authors claim to ... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and for recognizing the contribution of our work. Your feedback has been invaluable in enhancing the quality and clarity of our manuscript. We reply to the questions and weaknesses.
>**Q1&W1**: Impact of heterogeneity
When the client‘s data distribution is... | null | null | null | null | null | null |
Overcoming Vocabulary Mismatch: Vocabulary-agnostic Teacher Guided Language Modeling | Accept (poster) | Summary: This paper proposes a Vocabulary-agnostic Teacher Guided Language Modeling for guiding the training of smaller student models by large teacher models. This method tries to bridge the gap caused by vocabulary mismatch in different models. The proposed approach comprises two key components: Token-level Lexical A... | Rebuttal 1:
Rebuttal: Thank you for the reviewer’s helpful feedback. We are pleased that the reviewer recognized the motivation and analysis of our work. We provide our response as below.
### **Clarification of the “Vocabulary Mismatch” Notion.** (Claims And Evidence)
We appreciate the chance to clarify our terminol... | Summary: The paper addresses the challenge of vocabulary mismatches between teacher and student language models during knowledge distillation. I believe this is a well-motivated and important topic since it is difficult to do the logits-level distillation between student and teacher models with different tokenizers. To... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s insightful comments and valuable suggestions. We are glad that the reviewer acknowledged the importance of the problem and appreciated our comprehensive analysis. Below, we provide our detailed responses to the points raised.
### **Distinction between "Teach... | Summary: This paper proposes VocAgnoLM, a method to overcome vocabulary mismatch in knowledge distillation for language models. It introduces Token-level Lexical Alignment for precise token mapping and Teacher Guided Loss to adjust training signals. Experiments show up to 46% improvement over baseline methods, enabling... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's thoughtful feedback and constructive suggestion. We address the points raised below.
### **Impact of top-k threshold (Selected Ratio) on scaling trends.** (Claims and Evidence #1, #2)
- As discussed in Appendix B and Figure 7a, we explore the effect of the top-k thres... | null | null | null | null | null | null | null | null |
Blink of an eye: a simple theory for feature localization in generative models | Accept (oral) | Summary: This paper introduces a general framework for critical windows in stochastic localization. After a lengthy but valuable description of some key notions such as stochastic localization sampling and the "forward-reverse experiment", the authors prove their key result, which shows that there exist (possibly empty... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful comments. We were glad to hear that you thought that the theoretical results were interesting and that experiments were sound and illustrative of our main points.
## Writing changes
* *“While this paper is fairly well-written, it ... | Summary: This paper discusses the phenomenon of critical windows in generative models. It is an interesting topic, and the paper presents a general theory with minimal assumptions, enabling the explanation of abrupt shifts during the sampling phase across different modeling paradigms and data modalities. The writing is... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful comments. We were glad to hear that you found our writing clear and engaging and our theory interesting.
* *“If the reverse process is deterministic, such as an ODE, or includes additional conditions, such as text-to-image, does t... | Summary: The authors present a paper that explores critical windows in generative models. Their paper is heavily theoretical and they propose an understanding that can be applied to a wide range of models.
Claims And Evidence: yes
Methods And Evaluation Criteria: yes
Theoretical Claims: I did not check the accuracy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their kind comments and strong recommendation. We will modify the abstract to say ''hacks’’ instead of ''jailbreaks.’’
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Rebuttal Comment 1.1:
Comment: The system seems to require a rebuttal comment. Nothing new added here | Summary: The paper theoretically explains sudden behavioral shifts in generative models through critical windows, employing a forward-reverse experiment to study this phenomenon. It introduces Theorem 3.1, which bounds total variation distance to demonstrate that these windows signify transitions between sub-mixtures. ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments. We were glad to hear that you found that our experimental results for LLMs are convincing.
* *“Since LLM performance is sensitive to evaluation metrics, a deeper discussion on the robustness of critical windows across different metrics is ne... | Summary: The paper presents a theory of “critical windows” – intervals in the generation process in which specific features of the generated data emerge – in both diffusion and autoregressive systems. Leveraging the framework of stochastic localization, the authors rigorously characterize when such windows appear. The ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful comments, especially with respect to the contribution section and the exposition of our theory. We were glad to hear that you found that our rigorous unifying framework was interesting and that our experiments were well-executed.
#... | Summary: The paper investigates "critical windows" in generative models—brief intervals during the generation process in which features of the final output are determined. The authors introduce a general theoretical framework based on stochastic localization samplers, a class that includes both diffusion models and aut... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and thoughtful comments, especially with respect to our theory’s assumptions and the relationship between our experiments and theory. We are glad that you found that the generality of our theory and LLM reasoning experiments interesting.
**Modeli... | null | null |
KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search | Accept (poster) | Summary: This paper proposes KBQA-o1, which utilizes Monte Carlo Tree Search and ReAct-based agent process to generate stepwise logical form with knowledge base environment.The incremental finetuning strategy on automatically labeled examples further enhances the performance. According to the experiment results, KBQA-o... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper. We sincerely appreciate your feedback. Below, we respectfully provide our detailed responses to address your concerns.
**W1: The efficiency analysis is reflected by the number of queries per minutes, just wondering how many quer... | Summary: The paper introduces KBQA-o1, a novel agentic Knowledge Base Question Answering (KBQA) method that leverages Monte Carlo Tree Search (MCTS) to address challenges in KBQA, such as weak KB awareness, the trade-off between effectiveness and efficiency, and high reliance on annotated data. The proposed method empl... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper. We sincerely appreciate your feedback. Below, we respectfully provide our detailed responses to address your concerns.
**Q1: Limited Evaluation on Real-World Scenarios: While the paper demonstrates strong performance on benchmar... | Summary: This paper proposes a novel agentic KBQA framework that integrates Monte Carlo Tree Search (MCTS) with large language models (LLMs) to address challenges in low-resource and complex reasoning scenarios.
There are too many baselines not being discussed or compared, which makes this paper far from technical... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper. We sincerely appreciate your feedback. We understand that your main concerns center around two key aspects: **performance** and **efficiency**. Below, we respectfully provide our detailed responses to address these points.
**< P... | Summary: The paper presents KBQA-o1, an agentic Knowledge Base Question Answering (KBQA) method that integrates Monte Carlo Tree Search (MCTS) for improved logical form generation. It addresses challenges in KB awareness, search efficiency, and reliance on annotated data by employing a ReAct-based agent process and inc... | Rebuttal 1:
Rebuttal: Thank you very much for your time and effort in reviewing our paper. We sincerely appreciate your feedback. Below, we respectfully provide our detailed responses to address your concerns.
**Q1: Proposition 4.3 – There exists a reward threshold 𝛾∗ that ensures incremental fine-tuning improves mod... | null | null | null | null | null | null |
Improved and Oracle-Efficient Online $\ell_1$-Multicalibration | Accept (poster) | Summary: This paper tackles the challenge of online multicalibration. The key contribution of this paper is theoretical: the paper proposes a method that achieves improved rate of O(T^-1/3) and oracle efficient rate of O(T^-1/4). The key insight is that one can reduce the L1 multicalibration problem into an online line... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s concern regarding the theoretical nature of our work. While our contributions are indeed theoretical, we believe ICML is an appropriate venue for the following reasons. (1) Relevance to Core ML Problems: Multicalibration — and online multicalibration in particular — is... | Summary: The paper focuses on the online multicalibration task, for which it (1) presents a O(T^{2/3})-ECE error algorithm, thus matching the best known constructive efficient bounds for vanilla calibration; and (2) presents an oracle-efficient algorithm that obtains O(T^{3/4}) multicalibration ECE error given access t... | Rebuttal 1:
Rebuttal: We thank the reviewer for pointing us towards this relevant reference.
After reviewing their results, we agree that their framework can be used to derive bounds for online $\ell_1$-multicalibration, and we outline a high-level approach for binary-valued hypothesis classes below.
Fix a $h \in \mat... | Summary: The paper studies the problem of online multicalibration for L1 norm. The paper proposes a method with theoretical guarantees. The key contribution is based on the reduction of online L1-multicalibration to an online learning problem.
### update after rebuttal
I am maintaining the current score following the ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and positive evaluation of our paper. | Summary: The paper studies an online prediction setting, where a learner wished to minimize $\ell_1$ multicalibration error with respect to a class of real-valued predictors $\mathcal{H}$ that act as group selection functions. The authors propose an algorithm that obtains an error rate of $O(T^{-1/3})$, through reducin... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive evaluation of our paper, and for pointing us towards the paper [NRRX'23]. Please refer to the response to Reviewer~uPiD. | Summary: This paper studies the online l1-multicalibration problem. Multicalibration is a natural extension of calibration with group identities. It is a natural group fairness definition and implies some learning concept called omniprediction.
The authors improved based on a previous work that provides O(T^{1/4}) up... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thorough review of our paper (and for discovering a typo in Appendix~C). We also appreciate the reminder to acknowledge the known lower bound for online calibration, which indeed extends to online $\ell_1$- multicalibration.
To address the first comment, yes, our r... | null | null | null | null |
"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift | Accept (poster) | Summary: This paper proposes a nonparametric Subgroup-scanning Hierarchical Inference Framework for performance drifT (SHIFT) to use hypothesis testing for drift diagnosis. The SHIFT first decides if any subgroup experiences significant performance decay from drift, then checks the specific shift that explains the deca... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive feedback and are glad to know that they found the work to be well-written and to have **great potential in practical applications**. Indeed, SHIFT addresses a **critical and very practical question**: when performance of an ML algorithm drops in a new a... | Summary: This paper introduces SHIFT, a hierarchical hypothesis-testing framework designed to identify subgroups experiencing significant performance degradation in machine learning models due to distribution shifts. SHIFT first tests for the presence of large performance decay due to aggregate covariate and outcome sh... | Rebuttal 1:
Rebuttal: We thank the reviewer for their positive feedback appreciating the practical utility of the methods and for providing helpful suggestions on additional benchmarks. Indeed, SHIFT addresses a **critical and very practical question**: when performance of an ML algorithm drops in a new application con... | Summary: The paper titled "Who experiences large model decay and why?" introduces a hierarchical framework called SHIFT (Subgroup-scanning Hierarchical Inference Framework for performance drifT) to diagnose heterogeneous performance drift in machine learning models. The framework aims to identify subgroups that experie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments and are heartened to hear they appreciate the novelty of SHIFT and its ability to provide detailed explanations for subgroup-specific shifts. Indeed, SHIFT addresses a **critical and very practical question**: when performance of an ML algorithm drops in a ... | Summary: This paper proposes a method (SHIFT) for diagnosing performance drift in machine learning models that are transferred from a “source” to a “target” domain. Specifically, it aims to identify where (i.e., in which subgroups) a model’s performance decays the most and how such decay arises, distinguishing between ... | Rebuttal 1:
Rebuttal: We thank the reviewer for carefully reading the work and for appreciating its practical relevance. Indeed, heterogeneity in ML performance is a major safety concern in high-risk applications and there is no unified test to identify the sources of heterogeneity.
---
**Why SHIFT outperforms other... | null | null | null | null | null | null |
ALMTokenizer: A Low-bitrate and Semantic-rich Audio Codec Tokenizer for Audio Language Modeling | Accept (poster) | Summary: The paper introduces ALMTokenizer, a low-bitrate and semantically rich audio codec.
It incorporates a novel fixed-interval query interleaving mechanism which extracts contextual features from the acoustic features and quantizes (using RVQ) only the contextual features extracted by these queries, thus achievi... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions.
**Q1:** latent space optimized for AR modeling...it seems that not using the LM loss is beneficial for several metrics.
**A:** We appreciate this comment. We acknowledge that introducing the autoregressive (AR) loss may slightly impact r... | Summary: This paper introduces ALMTokenizer, a codec for speech, music and sound, which incorporates semantic and acoustic information into a single hierarchy of residual tokens with remarkable performance at a very low bitrate. The proposed improvement over previous codec include both architectural changes and trainin... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We do appreciate the constructive comments the reviewer provided to us to further improve our paper. We are delighted to have the following discussion with the reviewer.
**Q1:** One of the claims is that the "semantic priors" avoids distill... | Summary: The paper presents a method to convert an audio signal to a sequence of discrete tokens, with an aim to maximize compression (low bit rate) while retaining maximum semantic information. To achieve this goal, it introduces the use of learnable query tokens, masked auto-encoders, semantic priors (to initialize V... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's time and patience with our paper. We are delighted to solve the concerns of reviewer one by one.
**Q1:** Several essential ... query token is not clear to me.
**A:** We appreciate your feedback regarding the clarity of the "query token" concept. Below, we ... | Summary: The authors propose ALMTokenizer, an audio tokenizer designed to enhance compression efficiency and reconstruction quality at a low bitrate. Its key innovations include a query-based framework, semantic priors in vector quantization (VQ) codebooks by leveraging self-supervised learning (SSL) model feature clus... | Rebuttal 1:
Rebuttal: We thank the reviewer for recognizing our contributions. We do appreciate the constructive comments the reviewer provided to us to further improve our paper. We are delighted to have the following discussion with the reviewer.
**Q1** : Although the paper’s ablation study suggests that each propos... | null | null | null | null | null | null |
Safe Delta: Consistently Preserving Safety when Fine-Tuning LLMs on Diverse Datasets | Accept (poster) | Summary: The authors propose a new harmful fine-tuning (and benign fine-tuning) defence method that estimates a correction vector that is applied after training the model. They show that their method doesn’t harm utility while maintaining a low attack success rate.
Claims And Evidence: Within the scope of previous lit... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews and comments. We will address your concerns and questions as follows:
> C1: Much larger attack datasets should be considered. Can the authors please add an evaluation using more attack samples such as 10k?
Thank you for your thoughtful advice. We adopt expe... | Summary: The paper introduces Safe Delta, a two-stage method that estimates the effects of specific datasets on safety and utility, compensating the safety degradation while maintaining utility.
Claims And Evidence: The claims made by the authors are supported by the experimental results presented, though there are so... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews. We are glad that you found our work **novel, theoretically grounded, and clearly presented**. Below, we address your concerns:
> C1 & Q1: Baseline performance in PureBad differs from previously reported ones.
### SafeLoRA
We appreciate your careful observat... | Summary: This paper introduces a novel defensive method to enhance LLM safety after fine-tuning. Specifically, it proposed to Safe Delta, which consists of a preparation step performed before fine-tuning and two steps (Finding Delta Parameters, Adding Safety Compensation) executed for each fine-tuning request. The goal... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews and comments. We will address your concerns and questions as follows:
> C1: Reviewer did not really understand the correlation between the theorem and the Optimal Brain Surgeon, which is neuroscience terms.
Thank you for raising this questions.
This questio... | Summary: Safe Delta is a safety-aware post-training defense method that adjusts the delta parameters (i.e., the parameter change before and after fine-tuning). Safe Delta estimates the safety degradation, selects delta parameters to maximize utility while limiting overall safety loss, and applies a safety compensation ... | Rebuttal 1:
Rebuttal: Thank you for your insightful reviews. We appreciate your recognition of our work as **novel, effective and efficient**. Below, we address your concerns:
> C1 & Q3: Baseline performances on Llama3-8b-instruct
Thank you for your thoughtful advice.
Since all baselines release their code based on L... | null | null | null | null | null | null |
WAVE: Weighted Autoregressive Varying Gate for Time Series Forecasting | Accept (poster) | Summary: In this paper the authors integrate the attention mechanism used for time series forecasting with the concepts of moving average in used in classic statistical ARMA models. In particular they device the indirect MA weights on top of patched tokenized time series with the emphasis on linear attention level comp... | Rebuttal 1:
Rebuttal: > Pretrained decoder-only models & end to end evaluation
We'll clarify our introduction regarding design purpose and use this as evidence in Section 2.2 showing AR attention-based TSF models perform comparably to other structures.
> Intuition behind WAVE on a patched level
> Context of ARMA
> In... | Summary: The author incorporates a moving average term into the autoregressive attention model for linear attention mechanisms, achieving state-of-the-art performance.
Claims And Evidence: 1. Effectiveness of the decoder-only autoregressive Transformer
- In time series forecasting (TSF), the previously overlooked... | Rebuttal 1:
Rebuttal: > The method in this paper requires increasing the input length when the prediction length is extended. Even though, as the authors suggest, this method can be viewed as a patch for patchtst with added AR loss, patchtst itself does not require a longer input length when increasing the prediction l... | Summary: The paper proposes WAVE, a novel attention mechanism integrating autoregressive (AR) and moving average (MA) components for time series forecasting (TSF). The key contributions include:
Demonstrating that a decoder-only autoregressive Transformer, with proper tokenization and preprocessing, achieves performan... | Rebuttal 1:
Rebuttal: > It lacks rigorous theoretical proofs (e.g., asymptotic complexity analysis to substantiate WAVE’s linear time complexity claims)
Thank you for your suggestion. We provide the time complexity analysis below:
**Proposition** For a sequence of length $N$ and embedding dimension $d$, WAVE attentio... | Summary: The Weighted Autoregressive Varying Gate (WAVE) attention is a new mechanism that augments Transformer attention with both an autoregressive component and a moving-average component. By combining ideas from statistical models with efficient Transformer architectures, WAVE expands the modeling capacity for time... | Rebuttal 1:
Rebuttal: > The paper adequately cites recent Transformer-based TSF models, but lacks statistical forecasting approaches (e.g., deep state-space models and mamba, etc).
Thank you for your suggestion. We will add a related works section in our revision to discuss statistical forecasting and recent SSM-based... | Summary: This work introduces a decoder-only Transformer based model for time-series forecasting and introduces the WAVE attention mechanism. The WAVE attention mechanism leverages autoregressive and weighted moving averaging techniques. The authors show that coupling WAVE-based attention and a decoder-only structure o... | Rebuttal 1:
Rebuttal: > Describe how you avoid error accumulation.
Thank you for this question. To clarify, this approach is not a contribution but a **prerequisite setting** demonstrating that **pure AR attention** can match previous models. Traditional one-step AR models accumulate significant errors during iterativ... | null | null | null | null |
How Do Large Language Monkeys Get Their Power (Laws)? | Accept (oral) | Summary: This work tries to explain a curious phenomenon in LLM test-time scaling via repeated sampling and verification, as well as in Best-of-N jailbreaking:
while the per-problem failure probability should decay exponentially with the number of attempts,
it is often observed in practice that the average success rate... | Rebuttal 1:
Rebuttal: Thank you for your positive and thorough review of our work. We appreciate your thoughtful assessment of our theoretical and numerical analyses, as well as your recognition of our contribution in applying the mathematical insight about power laws emerging from weighted sums of exponential function... | Summary: The paper demonstrates that power law behaviour in “pass at k” metrics originates from a power law tail in the distribution of the “pass at 1” probability across the test set. Furthermore, it argues that directly modeling the “pass at 1” distribution leads to more accurate predictions for the values of “pass a... | Rebuttal 1:
Rebuttal: We appreciate your constructive feedback. We address your points below.
### Improvements to Related Work
> Could they provide a parametric form for that distribution? Polo et al. (2024) likely also deserve more detailed discussion due to the pass@k experiments they describe in Section 4.5.
We ... | Summary: This paper explores the scaling behavior of LLMs when inference-time compute is increased through repeated sampling. While failure rates for individual problems should decrease exponentially with multiple attempts, the authors observe that the aggregate success rate across many problems follows a power law. Th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review. We appreciate your recognition of our work's strengths, particularly that our paper "presents a novel theoretical framework explaining why per-problem success rates follow exponential decay, while aggregate success rates exhibit power law behavior" and that ou... | Summary: This paper investigates the negative log of the average success rate scales as a power law with the number of attempts when LLMs make multiple independent attempts at a task (mathematical problems or jailbreaking). The authors identify a paradox that for any individual problem, success rates should improve exp... | Rebuttal 1:
Rebuttal: Thank you for your thorough and thoughtful review of our work. We will correct the typo you identified in line 255, changing "Kuamraswamy" to "Kumaraswamy." We address other points below:
### Origins of Heavy Left Tailed Distributions
> The brief discussion of benchmark design and selection bias... | null | null | null | null | null | null |
NETS: A Non-equilibrium Transport Sampler | Accept (poster) | Summary: The authors propose a method for sampling from unnormalized probability distributions. The method builds on diffusion-based sampling, where a learnable drift is added to the stochastic differential equation. The authors propose a PINN objective which allows for off-policy optimization and does not require diff... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading of our paper and positive feedback. We are glad that you found the work well-written, theoretically sound, and that the numerical experiments demonstrate convincing evidence for our method's effectiveness. Below we address your comments and suggestio... | Summary: This paper introduces an algorithm for sampling from unnormalised probability distributions, through non-equilibrium sampling approaches. When computing expectations with respect to the final-time marginal distribution, classical approaches to this would leverage AIS or equivalently Jarzynski / Crooks. ... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive feedback. We are glad that you found the contribution novel and the theory sound and thorough. Below we address your comments and suggestions and supply more information on experimental results:
**Additional experiments**:
In the anonymous drive link https:... | Summary: The authors propose NETS, a Non-Equilibrium Transport Sampler that interpolates between two unnormalized densities $\rho_0$ and $\rho_1$ based on a user-defined choice of interpolant. A key contribution of the proposed approach is to introduce learning in the dynamics of continuous time annealed importance sam... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and we are happy to hear you found the work theoretically sound and novel. Below, we try to address all your questions, and provide some info on new additional experiments. We itemize our response according to the headings that appear in you revie... | Summary: This paper investigates sampling from a target distribution within the annealed importance sampling (AIS) framework. Inspired by Jarzynski equality, a continuous-time version of AIS can be formulated using an SDE for samples and an ODE for weights. Building on this, the paper proposes NETS by introducing an ad... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback, and we are happy to hear you found the work theoretically sound and novel. Below, we try to address all your questions, and provide some info on new additional experiments. We itemize our response according to the headings that appear in you revi... | null | null | null | null | null | null |
Rapid Overfitting of Multi-Pass SGD in Stochastic Convex Optimization | Accept (spotlight poster) | Summary: The author(s) analyzed the generalization lower bound of SCO in the multi-pass scenario. The lower bound that SCO with multi-pass can quickly overfit and yield $\Theta(1)$ population loss.
Claims And Evidence: Yes
Methods And Evaluation Criteria: Yes
Theoretical Claims: The proofs looks reasonable to me. I ... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and input. Below are our responses to the main comments in your review:
> “The theoretical result is some kind of inconsistent with practitioner's observation as multi-pass SGD usually doesn't really hurt the generalization. Better to add a limitation section to ... | Summary: This paper considers multi-pass SGD in the SCO setting. Single pass SGD is known to achieve optimal excess population error, but it was not clear how it performs in terms of population loss after multiple passes. In fact, this paper shows lower bounds indicating that (several versions of) multi-pass can quickl... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and feedback. We respond to main claims below:
> “a key part of the argument rests on Livni's 2024 connection between 'sample dependent oracles' and a standard SCO oracle. This connection is hardly explained at all”
We thank you for the comment - we will improve... | Summary: This work considers the Stochastic Convex Optimization (SCO) setting and investigates the excess population risk and sample complexity lower bounds for Stochastic Gradient Descent (SGD). While the majority of previous work tackled GD or single-pass SGD, this paper mainly focuses on the multi-pass version of SG... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and discussion. We respond to the main claims below:
> It is evident that the excess risk lower bound in Theorem 3.1 and Theorem 3.2 share an identical form with those in [Amir et al., 2021] and [Bassily et al., 2020]. Furthermore, as also acknowledged by the a... | Summary: The paper makes three main contributions in Stochastic Convex Optimization with convex and Lipschitz (but not necessarily smooth) loss functions: First, they establish tight bounds on the population excess risk of multi-pass SGD that apply to both single-shuffle and multi-shuffle variants. Second, they prove s... | Rebuttal 1:
Rebuttal: Thank you for the thorough review and discussion. Following are our responses to your main comments:
> “lacks empirical validation or real-world experiments to demonstrate the practical impact”
Our main contributions are theoretical and, respectfully, we disagree that this is a weakness. Theor... | null | null | null | null | null | null |
Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment | Accept (poster) | Summary: This paper investigates inference-time alignment in language models and demonstrates that naively scaling the Best-of-N heuristic leads to reward hacking, causing performance degradation beyond a certain computational threshold. The authors introduce a new algorithm, InferenceTimePessimism, which leverages χ²-... | Summary: This paper examines inference-time alignment, where additional computation at generation time is used to improve language model outputs. Specifically, the authors focus on the widely used best-of-$n$ approach, which generates multiple responses and selects the one with the highest reward according to a (possib... | Summary: The paper first theoretically shows the overoptimization problem is inevitable with the well-known best-of-N algorithm, especially when N increases. Then they propose Inference-Time Pessimism Algorithm, and show that the proposed algorithm resolves the overoptimization problem and also achieves optimal regret ... | Summary: The paper analyzes the Best-of-N (BoN) algorithm for selecting among language model generations and introduces InferenceTimePessimism, a new algorithm that mitigates reward hacking. The authors formalize inference-time alignment as improving a pre-trained policy’s responses using an imperfect reward model. The... | null | null | null | null | null | null | ||||
Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks | Accept (poster) | Summary: --- increased score from 2 to 3 after comment from authors ---
The authors developed a sample compression version of PAC Bayes generalization bounds, which reduce the number of training data points in standard PAC Bayes bounds into a compressed subset with generalization guarantee. They used a hypernetwork to... | Rebuttal 1:
Rebuttal: We thank the reviewer for his insightful feedback.
**1.** “People are applying bounds on models like 100M~7B parameters.”
Indeed, interesting works have successfully computed tight generalization bounds for large models. It does not undermine the need for tighter generalization bounds for small... | Summary: This paper proposes a novel meta-learning framework that uses PAC-Bayes and Sample compression theory to learn the hypernetwork parameters. The hypernetwork consists of two components: an encoder (or compressor) that maps the training set into the latent representation space, and a decoder (or reconstructor)... | Rebuttal 1:
Rebuttal: We thank the reviewer for his feedback, which will help us highlight the precise nature of our contribution. We undertake to add these clarifications to the manuscript.
**1. First concern (the theoretical results are not novel enough)**
**1.1.** We agree that our theoretical results are moderate... | Summary: The paper introduces new generalization bounds combining both PAC-Bayes and sample compression framework, and apply it in a meta-learning scheme. They introduce three different designs inspired by different theorems by using hypernetworks.
Claims And Evidence: Technically, the generalization bounds proved in ... | Rebuttal 1:
Rebuttal: We thank the reviewer for his careful reading of the paper.
**1. Claims And Evidence**
The reviewer correctly says that “the generalization bounds proved in this paper are not meta-learning generalization bounds.” Instead, our framework shows a new way of using generalization bounds in a meta-le... | Summary: The paper provides novel PAC-Bayesian bounds for meta-learning within the sample compression framework. The approach is based on the hypernetwork architecture. The paper also provides an experiment to show that the proposed bounds can be tighter than prior works. The key technical innovation is extending the s... | Rebuttal 1:
Rebuttal: We thank the reviewer for his careful reading of the paper.
**1. Other Strengths And Weaknesses**
**1.1.** Concerning the complexity of the proposed framework, the encoder-decoder architectures are central to our contributions; each component has its unique role in the whole. We are open to perf... | null | null | null | null | null | null |
An Online Adaptive Sampling Algorithm for Stochastic Difference-of-convex Optimization with Time-varying Distributions | Accept (oral) | Summary: In this paper, the authors propose an online adaptive sampling algorithm for solving nonsmooth DC problems under time-varying distributions.
Their major technique is the development of a convergence rate for the sample average approximation of subdifferential mapping.
Based on the technique, they show their a... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback. We address your comments as follows:
1. There are some other machine learning problems with a nonsmooth DC structure. It is well known that piece-wise linear functions are DC. In order to guarantee both robustness and continuity, they co... | Summary: The authors address the minimization of a function defined as the difference of two convex functions.
Moreover, these two convex functions are expressed as the expectations of random functions.
The authors then propose online estimators based on an adaptive sampling algorithm.
Claims And Evidence: The proofs ... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback. We address your concerns as follows:
1. We appreciate your comments on the simulation work. Our primary goal was to verify the theoretical validity of our method rather than to apply it to real data. The numerical result has demonstrated... | Summary: The paper studies stochastic difference-of-convex (DC) optimization. The analysis accounts for distribution shifts, and for non-smoothness of the components is derived, introducing some non-trivial technical contributions. The obtained algorithm is validated in a numerical experiment.
Claims And Evidence: The... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback. We address your questions as follows:
1. **Remark 2:** The main idea is that if there exists an isomorphic mapping between the probability spaces of the random variables $\xi$ and $\zeta$ associated with $G$ and $H$ (e.g., if $\xi$ and $... | Summary: This paper proposes algorithms for solving a stochastic DC program in a time-varying setting. Specifically, the distributions used to define stochastic convex components may vary over time and are assumed to converge to the true distributions. The proposed algorithm is a variant of the classic DC algorithm and... | Rebuttal 1:
Rebuttal: Thank you for your careful reading and valuable feedback. Below, we provide clarifications regarding the comments you raised:
1. **L019, right:** Thank you for pointing this out. In this paper, "critical point" specifically refers to a "DC critical point," following the convention in other DC p... | null | null | null | null | null | null |
Federated Causal Structure Learning with Non-identical Variable Sets | Accept (poster) | Summary: The paper introduces FedCDnv, a novel algorithm for federated causal discovery where clients observe non-identical but overlapping variable sets. A key challenge in this scenario is the spurious dependencies introduced by non-overlapping variables. To address this, the paper proposes a two-level priority selec... | Rebuttal 1:
Rebuttal: $\textbf{Responses for “Questions For Authors” are as follows.}$
$\textbf{R1}$. Our method handles distributed data with varying samples distributions, where the observed variable sets are non-identical. We also experimentally evaluate the impact of $\delta$ on FedCDnv's performance. A lower $\de... | Summary: This paper proposes novel algorithm FedCDnv, a federated method for learning causal structure where different clients observe non-identical variable sets. It mainly addresses two challenges: 1) spurious dependencies introduced by non-overlapping variable pairs, which may lead to incorrect causal conclusions, a... | Rebuttal 1:
Rebuttal: $\textbf{Responses for “Questions For Authors” are as follows.}$
$\textbf{Q1}$. Does the term “an oracle of conditional independence tests” refer to perfectly accurate CI tests?
$\textbf{R1}$. Yes, “an oracle of conditional independence tests” refers to fully accurate CI tests.
$\textbf{Q2}$. C... | Summary: The paper introduces FedCDnv, a federated causal structure learning algorithm designed for scenarios where clients have non-identical but overlapping variable sets. The method introduces theoretical criteria to distinguish definite causal and non-causal relationships. A two-level priority selection strategy (P... | Rebuttal 1:
Rebuttal: $\textbf{Responses for “Questions For Authors" are as follows.}$
$\textbf{Q1}$. Could the authors provide a real-world motivating example where FCD is used/needed and there are non-overlapping variables observed by clients?
$\textbf{R1}$. Thanks for your comment. A real-world example where non-o... | Summary: This paper investigates federated causal structure learning, aiming to discover causal relationships between variables from data distributed across individual clients while considering privacy concerns. The paper addresses federated causal structure learning with non-identical variable sets and designs an effe... | Rebuttal 1:
Rebuttal: $\textbf{Q1}$. Theorems 3.2 and 3.3 only indicate that the relationship between variables X and Y is uncertain, but do not analyze under what conditions their relationship can be determined.
$\textbf{R1}$. Thanks very much for your comment. As stated in line 114 of the manuscript, we initially as... | null | null | null | null | null | null |
Large Language-Geometry Model: When LLM meets Equivariance | Accept (poster) | Summary: EquiLLM integrates Large Language Models (LLMs) with geometric Graph Neural Networks (GNNs) to improve 3D structure and dynamics prediction. It uses an LLM for invariant feature processing, a GNN for equivariant encoding, and an adapter to ensure equivariance while leveraging external knowledge. Experiments sh... | Rebuttal 1:
Rebuttal: We sincerely thank you for the time and careful consideration you have given to providing detailed and constructive feedback. Your valuable insights have greatly improved both the technical accuracy and clarity of our manuscript. We have meticulously revised the paper to incorporate your suggestio... | Summary: This paper presents a method for solving equivariant tasks by combining a pre-trained large language model (LLM) with a trained, geometric graph network. The large language model is prompted only with invariant quantities, which come from both a natural language prompt and learned invariant features from the g... | Rebuttal 1:
Rebuttal: We are deeply grateful for the time and effort you have dedicated to offering valuable feedback. We have revised the paper to address all your comments. Below, we respond to each of your points in detail.
**Questions in Claims And Evidence**
> Q1:
In our experiments, we evaluated multiple LLMs ... | Summary: This paper puts forward EquiLLM, a strategy to merge large language models (LLMs) with geometric (E(3)-equivariant) graph neural networks (GNNs). The motivation is straightforward: GNNs with built-in physical symmetry can handle 3D data in a rotation-, reflection-, and translation-consistent way, but they typi... | Rebuttal 1:
Rebuttal: We sincerely appreciate your recognition of our work! We are deeply grateful for the time and thoughtful effort you have dedicated to offering such detailed and constructive feedback. Your valuable suggestions have significantly enhanced both the scholarly rigor and presentation of our manuscript.... | Summary: The authors propose EquiLLM – a framework designed to enhance spatial reasoning in 3D structure and dynamics by integrating geometry-aware prompting and equivariant Graph Neural Network layers. Experiments on molecular dynamics, human motion, and antibody design demonstrate are carried out, and show good perfo... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time and effort you have devoted to providing detailed and constructive feedback. Your insightful comments have been invaluable in improving both the technical quality and clarity of our manuscript. We have carefully revised our paper to incorporate your suggestions. Be... | null | null | null | null | null | null |
Implicit Subgraph Neural Network | Accept (poster) | Summary: This paper proposes a bi-level optimization framework for subgraph-level predictive tasks, where the outer level is to minimize the subgraph-level prediction loss, and the inner-level is to enforce the fixed-point conditions of the implicit representation of the subgraphs, so that they don't have to rely on ri... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's feedback.
---
### Time Consumption
Yes, EIGNN requires a preprocessing step. Our method incorporates a pretraining stage. Below is a comparison of the total runtime (in seconds) for both methods on the PPI_BP dataset. The reported values are the means over 10 runs. ... | Summary: This paper introduced ISNN, the first implicit model for subgraph representation learning, along with a provably convergent bilevel optimization algorithm for training. The proposed ISNN also integrates label-aware subgraph-level information. This paper converts the fixed-point iteration into bi-level optimiza... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s detailed feedback. Below are our responses addressing each concern:
---
## Logic and Motivation Problems
In the revised manuscript, we will include an expanded discussion on why implicit graph neural networks are particularly suited for subgraph learning tasks. We w... | Summary: This paper combines the information from both subgraphs and nodes to form a hybrid graph to tackle the subgraph-level graph learning problem. Instead of directly training a GNN on the hybrid graph, it uses implicit GNN combined with a bilevel optimization way to enhance model performance. Convergency guarant... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback. Our responses are as follows:
## Motivation
While traditional graph neural networks focus on node-level or entire graph-level representations, many real-world problems require understanding the structure within parts of a graph. Subgraphs often represent ... | Summary: The paper introduces the Implicit Subgraph Neural Network (ISNN), an innovative approach designed to enhance subgraph representation learning. ISNN is the first to use implicit neural network models explicitly for subgraphs, addressing limitations in existing methods, particularly concerning capturing long-ran... | Rebuttal 1:
Rebuttal: We thank you for your valuable feedback. Our responses are as follows:
---
## Sensitivity to Subgraph-Level Graph Construction Methods
In our experiments, we previously compared four subgraph-level graph construction methods—**random**, **neighborhood**, **position**, and **structure**. The goa... | null | null | null | null | null | null |
Causal-PIK: Causality-based Physical Reasoning with a Physics-Informed Kernel | Accept (poster) | Summary: The paper presents Causal-PIK, a novel method for causality-based physical reasoning that leverages a Physics-Informed Kernel within a Bayesian optimization framework. The primary focus is on single-intervention physical reasoning tasks, where an agent must make decisions based on the causal effects of its act... | Rebuttal 1:
Rebuttal: We thank reviewer d7h6 for their thoughtful feedback. We are glad they found the method addresses an important aspect of physical reasoning, that is learning from feedback. We address the reviewer’s comments and will incorporate all of the following discussions in the final draft.
> [d7h6.1] Resi... | Summary: The paper attempts to address the challenge of single-intervention physical reasoning tasks. It proposes Causal-PIK, which combines Bayesian optimization and a Physics-Informed Kernel. The method leverages physical intuition and causality to iteratively find optimal actions. Experimental results on the Virtual... | Rebuttal 1:
Rebuttal: We thank reviewer vdey for their thoughtful feedback. We are glad they found the method implementation to be well-executed with a design that effectively captures important physical intuitions. We address the reviewer’s comments and will incorporate all of the following discussions in the final dr... | Summary: This paper proposes a method, Causal PIK, using Bayesian optimization for causal reasoning via a Physics-Informed Kernel, in order to obtain an expressive posterior distribution over the environment dynamics.
Unlike prior works directly using a learned dynamics model to choose actions, Causal-PIK uses dynamic... | Rebuttal 1:
Rebuttal: We thank reviewer nzda for their thoughtful feedback. We are glad they consider the chosen benchmarks to be relevant for the task at hand. We address the reviewer’s comments and will incorporate all of the following discussions in the final draft.
> [nzda.1] High variance in the results presented... | Summary: The paper introduces Causal-PIK, a novel approach that integrates a Physics-Informed Kernel with Bayesian Optimization to reason about causality in single-intervention physical reasoning tasks. Experimental results on Virtual Tools and PHYRE physical reasoning benchmarks verify the proposed method could finish... | Rebuttal 1:
Rebuttal: We thank reviewer jF9J for their thoughtful feedback. We are glad they consider our method to be clearly presented with a sound experimental design which includes good ablation studies. We address the reviewer’s comments and will incorporate all of the following discussions in the final draft.
> ... | null | null | null | null | null | null |
Propagate and Inject: Revisiting Propagation-Based Feature Imputation for Graphs with Partially Observed Features | Accept (poster) | Summary: This paper identifies the problem of having low-variance channels after diffusion with mostly missing values. This happens when the available states are very similar. They propose adding random features to those channels and restarting the diffusion process with these synthetic features and the original low-va... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s detailed and perceptive comments.
First of all, we would like to clarify that propagation-based imputation methods for graph learning with missing features are designed to assign values to missing features in a way that improves downstream task performance. A... | Summary: This paper targets missing data imputation for graph data. The authors highlighted that existing propagation-based methods produce nearly identical values within each channel and they contribute little to graph learning. To resolve this limitation, the authors propose a propagation-based imputation scheme that... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s positive evaluation of our work and the absence of noted weaknesses. We thank the reviewer for recognizing that the claims in our paper are supported by clear and convincing evidence, and for highlighting the clarity of the writing, the soundness of the theor... | Summary: This paper addresses the issue of missing features in graph data, which hinders the effectiveness of Graph Neural Networks (GNNs). Existing diffusion-based imputation methods often result in low-variance channels, where feature values across nodes are nearly identical, leading to poor performance in downstream... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful questions and valuable suggestions to further improve our work.
> **Q1.** This work seems to show its advantage in especially large missing rate, such as 0.995 and 0.999. However, such a large missing rate is impractical in real applications.
**... | Summary: This work identifies a limitation of previous works for learning on graphs with missing features, that being the output channels for feature imputation have low-variance. To solve this problem, the authors diffuse the observed features with injected random noise to produce final imputed features. Their method,... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer’s positive feedback on the strength of our empirical results and theoretical justifications. We also appreciate the insightful questions, which provide valuable guidance for further enhancing our paper.
> **Q1.** Is there any theory to corroborate the reason tha... | Summary: In this paper, the authors introduce FISF, a novel approach for graph feature imputation. FISF effectively mitigates the low-variance channel problem by strategically injecting synthetic features, thereby enhancing performance in both semi-supervised node classification and link prediction tasks across a wide ... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the detailed feedback and insightful questions that help us further improve our work.
> **Q1.** Since I'm not familiar with this field, are there any other more advanced baselines?
We appreciate the reviewer’s thoughtful question. **Before submitting the paper... | null | null | null | null |
Hidden No More: Attacking and Defending Private Third-Party LLM Inference | Accept (poster) | Summary: The paper investigates the vulnerabilities of private inference in large language models (LLMs). Recent organizations rely on third-party LLM inference services when deploying large models locally due to resource constraints. These setups raise significant privacy concerns as user prompts will be disclosed to ... | Rebuttal 1:
Rebuttal: Thank you for your review and for recognizing Cascade's effectiveness against the vocab-matching attack (VMA). We address your comments below.
**W1**
Regarding white-box access: we clarify this differs from open-weights. Our setting involves access to permuted hidden states and weights, but not ... | Summary: This paper investigates privacy vulnerabilities in third-party LLM inference services, focusing on an open-weight setting. The authors first propose a vocabulary-matching attack, which can recover original user prompts from intermediate hidden states with near-perfect accuracy and remains effective against var... | Rebuttal 1:
Rebuttal: Thank you for your review. We are glad that you found our attack effective, and found our experimental work to be extensive. We address your comments below.
**W3**
Thank you for raising this important point. We have now run scaling experiments to investigate the effect of model size on the perfo... | Summary: This manuscript explores the field of private inference and proposes a vocabulary-matching attack that exploits hidden states to recover the original input of an LLM. The authors highlight that existing permutation-based and noise-based schemes fail to provide sufficient security against such an attack. To add... | Rebuttal 1:
Rebuttal: Thank you for your detailed review of our submission. We appreciate that you found our paper effectively identifies potential security risks in existing privacy-preserving schemes and that our work is comprehensive in coverage.
**W4**
We agree with your point. We have now modified the implementa... | Summary: This paper proposes a vocabulary matching attack by exploiting the autoregressive characteristics of the generative model, which can attack the privacy-preserving large language model (LLM) inference framework based on permutation and noise under the assumption that the model parameters are public. At the same... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We are glad that you found our proposed attack to be effective in the open-weights setting, and that Cascade is novel. We provide responses to some of the points you have raised below.
**W1 + Q1**
A slight extension of our vocab-matching attack can additionall... | null | null | null | null | null | null |
GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration | Accept (poster) | Summary: Following the widely used quantization framework GPTQ, this work identifies the problem in GPTQ named symmetric calibration that emerges from the per-layer optimization scheme. To tackle these challenges, this work proposes a unique calibration pipeline based on asymmetric calibration, which fully considers th... | Rebuttal 1:
Rebuttal: Thanks for your comments and positive feedback. Please check our response below.
>1. Bitwidth settings are limited to W4A4/W2A4. Considering the need for near-lossless quantization of LLMs/ViTs in some application scenarios, W6A6/W8A8 results would be a plus.
Thank you for this suggestion. We h... | Summary: Authors propose a novel fine-tuning free quantization framework GPTQv2 for LLMs. Authors first analyze the problem of previous "symmetric calibration"using optimal brain compression to derive a close-formed solution, and propose a novel "asymmetric calibration" to take quantization error as well as the accum... | Rebuttal 1:
Rebuttal: Thank you very much for your comments and thorough review. Please check our response to your questions.
>1. What does the lambda in eq.10 represent for? Is it a hyper-parameter? If so, then why there is a gradient on it?
$\lambda$ is the Langrange multiplier, which is not a hyperparameter. By t... | Summary: The authors introduce a new quantization method, GPTQv2. The key innovation here is the development of an asymmetric calibration approach, differing fundamentally from GPTQ, by explicitly aligning the quantized layer's outputs to the original, full-precision activations. They derive a closed-form solution usin... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment of our theoretical foundation and experimental results. Regarding your concern about hardware-level deployment and overhead analyses, we would like to clarify that GPTQv2 maintains full compatibility with GPTQ's quantization format since we did not modify the... | Summary: This paper proposed a modification to the widely-used GPTQ method. The main idea is that instead of minimizing the differences between quant(W)*A and W*A, authors proposed to minimize the differences between quant(W)*A with W*A_fp, i.e. its counterpart in the unquantized model. As in typical PTQ works, "sequen... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback on our manuscript. We appreciate your interpretation of GPTQ as a "sequential PTQ style" method versus the "distillation style" of our GPTQv2. Please see our responses to your specific concerns below:
>1. The terms "asymmetric calibration" and "symmetric calib... | null | null | null | null | null | null |
Boosting Protein Graph Representations through Static-Dynamic Fusion | Accept (poster) | Summary: This manuscript proposes a simple relational heterogeneous GNN model to represent both structural information and molecular dynamics correlations of proteins. It validates the effectiveness of approach in multiple protein graph representation related tasks simultaneously.
Claims And Evidence: I think the clai... | Rebuttal 1:
Rebuttal: **To Weakness 1:**
We acknowledge the reviewer's concern about technical novelty. Our straightforward framework bridges static structures and dynamic correlations from molecular dynamics—a growing need as such data becomes increasingly available. The intentional simplicity of our approach is actu... | Summary: The authors propose to integrate structural and dynamic distance-based features into relational graph neural networks to predict local and global properties of 3D protein biomolecules. The authors' experiments are comprehensive and informative, and this work outlines a notable gap in the literature on protein ... | Rebuttal 1:
Rebuttal: **To Weakness 1:**
We appreciate the reviewer's feedback. While our approach appears straightforward, its significance lies in bridging the gap between static structural information and dynamic behavior in protein representation.
To address concerns about experimental depth and generalizability,... | Summary: The paper introduces a novel graph representation technique that integrates both static structural information and dynamic correlations from molecular dynamics (MD) trajectories for enhanced protein property prediction. This technique combines relational graph neural networks (RGNNs) with a dual approach:Dista... | Rebuttal 1:
Rebuttal: **Essential References Not Discussed**
> Dynamical surface representation methods, like [1]. [1] Sun, D., Huang, H., Li, Y., Gong, X., & Ye, Q. (2023). DSR: dynamical surface representation as implicit neural networks for protein. Advances in Neural Information Processing Systems, 36, 13873-13886... | null | null | null | null | null | null | null | null |
AutoCATE: End-to-End, Automated Treatment Effect Estimation | Accept (poster) | Summary: In this paper, authors have developed and released an AutoML library, called AutoCATE, for the automated selection and hyperparameter tuning of meta-learners for CATE estimation. They divide the CATE development pipeline into evaluation, estimation and ensembling phases, where evaluation corresponds to choosin... | Rebuttal 1:
Rebuttal: Thank you for your thorough review!
___
## Related work
While prior work automates parts of CATE estimation, no approach–to our knowledge–provides an end-to-end automated framework. Existing work only automates part of our approach (e.g., learning pseudo-outcomes with AutoML). In contrast, AutoCAT... | Summary: This paper presents AutoCATE, an automated, end-to-end framework for estimating Conditional Average Treatment Effects (CATE). The core motivation is that while ML methods have made significant advancements in causal inference, their adoption remains limited due to the complexities in pipeline selection, hyperp... | Rebuttal 1:
Rebuttal: Thank you for your insightful review–this is highly appreciated!
## Real-world data and violations
We agree that these are crucial considerations. While we use real-world data to validate AutoCATE as much as possible (e.g, Twins and the uplift data in Appendix D.4), we acknowledge that more real-w... | Summary: The authors propose a pipeline for automating the several design choices required for CATE estimation; from preprocessing datasets to different risk measures for model selection. The pipeline is divided into three stages corresponding to the following three questions; what risk measure should be used for model... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in reviewing our work!
## Obtaining the best meta/base-learner and training nuisance models with AutoML
Please allow us to clarify our approach. AutoCATE follows an AutoML-based procedure to tune ML pipelines at multiple stages: first, to optimize risk measures ... | Summary: The paper presents AutoCATE, an automated framework for CATE estimation, optimizing model selection, tuning, and validation via counterfactual Combined Algorithm Selection and Hyperparameter (CASH) optimization. It unifies evaluation, estimation, and ensembling, automating key design choices for improved gener... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review!
## Robustness to selection bias
We agree that synthetic data allows for precise control over selection bias and covariate shift, enabling a more systematic evaluation.
We have added a synthetic experiment where we vary the degree of selection bias (controlled ... | null | null | null | null | null | null |
PILAF: Optimal Human Preference Sampling for Reward Modeling | Accept (poster) | Summary: This paper introduces PILAF (Policy-Interpolated Learning for Aligned Feedback), a novel sampling strategy for iterative/online DPO. The authors show that with this new sampling algorithm, the gradient of the loss function matches the KL-regularized objective function, and they further provide asymptotic analy... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review, particularly for recognizing the strength of both the theoretical and experimental parts, and for checking the proofs of all theoretical results.
1. > "Value-Incentivized Preference Optimization". The authors should also add this work into discus... | Summary: The paper "PILAF: Optimal Human Preference Sampling for Reward Modeling" introduces Policy-Interpolated Learning for Aligned Feedback (PILAF), a novel sampling strategy designed to improve reinforcement learning from human feedback (RLHF), particularly in reward modeling for aligning large language models (LLM... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time in providing the review.
1. > HH-RLHF not enough. More benchmarks.
Please allow us to put our work into more context. Our contributions extend beyond simply empirically validating a new sampling algorithm. Rather, first, we identify and rigorously characteriz... | Summary: This paper investigates strategies to leverage interpolated response sampling for improving human preference data collection and reward modeling in RLHF. The authors propose a Policy-Interpolated Learning for Aligned Feedback PILAF method that generates response pairs by interpolating between a reference polic... | Rebuttal 1:
Rebuttal: Thanks for appreciating our work, especially its development from theory to algorithm design. We are sincerely pleased that the reviewer aacknowledged the misalignment problem we identified, found the combination of mathematical proofs and experiments to provide a reasonable justification, and con... | Summary: This paper introduces a sampling strategy for collecting human preference data in RLHF (specifically, DPO) setting. It aims to align preference-based reward modeling with the true (oracle) objective by interpolating between the current and reference policies during response generation. Theoretical analysis sho... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful comments, which have helped improve the presentation of the misalignment problem. We are also glad that the reviewer enjoyed our theoretical analysis and empirical validation.
1. > uniform sampling - misaligned gradients
We thank the reviewer for raising t... | null | null | null | null | null | null |
Star Attention: Efficient LLM Inference over Long Sequences | Accept (poster) | Summary: This paper proposes Star Attention, which improves the LLM inference efficiency by sharding attention across multiple hosts.
Claims And Evidence: Please see **Other Strengths And Weaknesses**.
Methods And Evaluation Criteria: Please see **Other Strengths And Weaknesses**.
Theoretical Claims: Not applied her... | Rebuttal 1:
Rebuttal: We thank Reviewer dKGV for their detailed and insightful feedback. Below, we respond to the concerns regarding system performance analysis, literature coverage, compatibility, and presentation.
### **1. System Performance Analysis:**
- While we agree that kernel-level profiling and analysis acros... | Summary: The paper introduces StarAttention, a sparse attention method for encoding long-context by distributing chunks of context over GPUs. Unlike Ring Attention, Star Attention uses only local (in-chunk) attention for the majority of the context, allowing for a substantial speedup. Each block attends only to itself... | Rebuttal 1:
Rebuttal: We thank the reviewer EMsM for their insightful comments and suggestions. We address each point below:
### **1. Clarity of Abstract Wording:**
Thank you for pointing out the ambiguity in the abstract's final sentence. You are correct that the “up to 11x” improvement specifically refers to inferen... | Summary: This paper propose star-attention which combines a streamingllm attention for the prefill stage and a dense attention for the decoding stage. Specifically, the author implement the streamingllm pre-fill with blocks, where the computing are partioned across the query dimension. The sink and local blocks are pac... | Rebuttal 1:
Rebuttal: We thank the reviewer dnhh for their feedback and acknowledge their points regarding novelty, motivation, baselines, and benchmarks. We address these points below:
### **1. Novelty and Relation to Prior Work:**
- While Star Attention draws inspiration from prior work like StreamingLLM and attenti... | Summary: This paper presents Star Attention, a novel two - phase block - sparse approximation algorithm for efficient LLM inference over long sequences. The self - attention mechanism in Transformer - based LLMs has quadratic complexity, making long - sequence inference costly and slow. Star Attention addresses this is... | Rebuttal 1:
Rebuttal: We appreciate the reviewer x5MA's constructive feedback. Below, we address the identified weaknesses regarding anchor blocks, performance on complex tasks, and block size dependency:
### **1. Role and Configuration of Anchor Blocks:**
- **Anchor Block Size:** The performance peak when the anchor ... | null | null | null | null | null | null |
REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective | Accept (poster) | Summary: The paper demonstrates that existing LLM jailbreak defenses significantly underestimate model vulnerability due to non-adaptive attack objectives. By adopting a reinforcement learning-based approach, adversarial attacks can become more effective and adaptive, posing a greater challenge for safety alignment eff... | Rebuttal 1:
Rebuttal: We thank the reviewer for the suggestion and plan to investigate theoretical guarantees in future work.
## Alternative evaluation strategies
While our objective might utilize false positives of the judge instead of triggering actually harmful behavior, such cases clearly do not appear systematic... | Summary: This paper addresses the challenge of jailbreaking large language models (LLMs) – i.e. crafting adversarial prompts that make an aligned (safety-trained) model produce disallowed or harmful content. The authors point out a key limitation in current adversarial prompt attacks: they typically optimize a static o... | Rebuttal 1:
Rebuttal: ## Mitigation
The robustness literature suggests that only systematic methods like adversarial training actually help. For adversarial training, the attack effectivity is key for actual improvements (e.g., see [Kolter and Madry, 2018](https://adversarial-ml-tutorial.org/adversarial_training/) arg... | Summary: The paper "REINFORCE Adversarial Attacks on Large Language Models: An Adaptive, Distributional, and Semantic Objective" presents a novel approach for adversarial attacks on large language models (LLMs). Traditional optimization-based adversarial attacks rely on maximizing the likelihood of a predefined affirma... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and the numerous suggestions! We will address the points made in a revised version of the paper. Next, we elaborate on some of the points and answer the questions.
## The reliance on LLM-as-a-judge evaluations may introduce biases in measuring attack success... | Summary: The authors propose a new text-based adversarial loss function for jailbreak attacks, addressing the limitation that optimizing solely for affirmative responses (e.g., "Sure, here is how to...") can lead to non-harmful completions. To improve effectiveness, the authors introduce a loss function that incorporat... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough feedback!
## Existing methods that optimize to avoid rejections
We thank the reviewer for pointing out works that avoid rejections. While we have already referenced the mentioned work, we have not explicitly discussed this alternative objective. In a revise... | null | null | null | null | null | null |
Componential Prompt-Knowledge Alignment for Domain Incremental Learning | Accept (poster) | Summary: Domain Incremental Learning (DIL) is crucial for processing data across different domains while maintaining previously acquired knowledge, but current prompt-based methods suffer from misalignment issues when integrating knowledge from different domains. The authors identify that this problem stems from random... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback and recognition. Below are our responses, which we hope effectively address your concerns.
**Q1-1: Requirement of maintaining a prompt pool.**
(1) Maintaining a prompt pool is not a specific requirement of KA-Prompt but is already present in the baseline ... | Summary: This paper focuses on the domain incremental learning (DIL) task and identifies component-wise misalignment between domain-specific prompts as a key factor that leads to conflicting knowledge integration and degraded predictions in prompt-based DIL methods. To address this issue, the authors propose the Compon... | Rebuttal 1:
Rebuttal: We sincerely appreciate the reviewer’s constructive feedback and recognition. We hope the following responses effectively address your concerns.
**W1: Performance analysis**
(1) Both KA-Prompt and the C-Prompt baseline exhibit lower performance on the initial domain compared to other prompt-base... | Summary: This paper introduces a novel component-based prompt knowledge alignment method, KA-Prompt, for Domain Incremental Learning (DIL). Its key contribution lies in addressing the cross-domain prompt misalignment problem, which is claimed to be a major limitation of existing prompt-based DIL methods, such as C-Prom... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback and comments. We hope the following responses address your concerns.
**W1: Contributions on framework**
The C-Prompt baseline corresponds to our New Prompt Training branch. We have made two key designs to form a brand-new DIL framework:
(1) Reusable Knowledge Mi... | Summary: The paper addresses the challenge of DIL. The authors identify a limitation in existing prompt-based methods: component-wise misalignment between domain-specific prompts leads to conflicting knowledge integration and degraded predictions. To address this, they propose KA-Prompt, a method that enforces compone... | Rebuttal 1:
Rebuttal: Thanks for the valuable feedback. We hope our responses address your concerns.
**W1: Misalignment's occurring and definition**
(1) Misalignment occurs during cross-stage prompt fusion in DIL. In Fig. 2, each prompt (e.g., $p_t^1$) consists of 4 tokens, each encoding distinct partial knowledge of... | null | null | null | null | null | null |
Generative Human Trajectory Recovery via Embedding-Space Conditional Diffusion | Accept (poster) | Summary: This paper proposes a conditional diffusion-based method for human trajectory recovery from incomplete or missing data. The authors aim to address the limitations of existing methods in capturing complex spatial-temporal dependencies and handling irregular sampling in human mobility data. DiffMove first transf... | Rebuttal 1:
Rebuttal: Thanks for your detailed feedback.
Claim&Evidence, Rela To Literature:
We introduce critical innovations that distinguish it from existing works:
a)Handling Discrete Locations via Embedding-Space Diffusion. Existing diffusion models for trajectories (e.g., DiffTraj) focus on generating continu... | Summary: The paper proposes the model DiffMove which is a conditional diffusion-based method for human trajectory recovery that leverages embedding denoising.
Claims And Evidence: I am confused about the research questions or challenges raised in this paper. I have listed them in detail in the question section.
Metho... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback.
W2:
They are shown as the first two equations in eq 6. Actually this is to treat trajectory embeddings as sessions and $e_0^{ob}$ and $e^{hist}$ are integrated using session based graph methods (Xu et al., 2019) and explained in Appendix A.1. We will revise... | Summary: This paper introduces DiffMove, a conditional diffusion-based model for recovering missing locations in sparse human mobility data. By converting discrete trajectory locations into a continuous embedding space, DiffMove effectively denoises and reconstructs missing locations through an embedding decoder. The m... | Rebuttal 1:
Rebuttal: Thank you for your detailed feedback.
Claims&Evidence:
We clarify how existing experiments explicitly demonstrate DiffMove’s superiority in complex, irregular, and uncertain scenarios:
1. Probabilistic Generation vs. Deterministic: Table 1 and 3 shows that sampling multiple trajectories (DiffMo... | Summary: This paper introduces DiffMove, a new framework for recovering human trajectory data based on conditional diffusion model design. DiffMove effectively handles complex spatial-temporal patterns in low-sampling data. It works by transforming trajectory locations into an embedding space, denoising the embeddings,... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and stating our evaluations on multiple datasets are good, claims are clear and convincing. Our responses to other parts are as below:
W(i):
Recovering sparse human trajectories, particularly those involving Points of Interest (POIs), significantly enhances vari... | null | null | null | null | null | null |
Mechanisms of Projective Composition of Diffusion Models | Accept (poster) | Summary: The paper proposes a theory for understanding composition in diffusion models and how it can produce samples that are out of distribution for each of the constituent models. Their key insight is that composition of distributions is ill specified unless tied to a projection that specifies which attribute we wou... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support for our paper and helpful suggestions.
* The reviewer suggests a quantitative analysis of our CLEVR experiments. We agree that this is an excellent idea and have performed the analysis. The results are shown in the table below and will be included in the c... | Summary: The authors present a formalization of compositionality in diffusion models. Using diffusion models separate for particular objects and background can be joined together in various ways. The authors explore these different ways and point out the correct way of composing these. The authors suggest a particular ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time, insightful questions, and constructive critiques.
Overall, we want to emphasize that the goal of this paper is to understand and predict when composition will work — and just as importantly, when it will fail. That is, we want to theoretically explain prior e... | Summary: This paper gives a rigorous theoretical framework for understanding composition in diffusion models, with a focus on out‐of‐distribution extrapolation and length‐generalization. The authors introduce the notion of “projective composition,” which formalizes the idea that a composed distribution should, when vie... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support for our work and insightful questions, to which we respond individually below.
Weaknesses
Q1: The Factorized Conditional assumption, critical for the theoretical guarantees, may be too strong and not fully reflective of practical scenarios.
* Theorem 6.1 ... | Summary: This paper proposes a new theoretical framework for analyzing a special type of composition in diffusion models, and it specifically focuses on two previously discovered phenomena in diffusion model composition: out-of-distribution (OOD) extrapolation and length-generalization. The theoretical framework aims a... | Rebuttal 1:
Rebuttal: We thank the reviewer for their support for our work and insightful questions, to which we respond individually below.
Weaknesses:
Q1: The main theoretical results only cover sampling in the pixel space, and a theoretically successful result is lacking in the feature space. Yet, feature space co... | null | null | null | null | null | null |
Likelihood-based Finetuning of Protein Language Models for Few-shot Fitness Prediction and Design | Reject | Summary: The authors want to use pre-trained protein language models for supervised prediction. Rather than classical fine-tuning to maximize regression accuracy with a linear probe, these authors suggest fine tuning the ordering of the likelihoods, which are good zero-shot predictors. Predictably, this method works we... | Rebuttal 1:
Rebuttal: Thank you very much for your feedback - we appreciate the review. Thank you for acknowledging the simplicity and applicability of our fine-tuning approach as a strength, whilst other reviewers discounted those same traits. Responses to your comments are given below. If we have sufficiently address... | Summary: This paper extends a ranking-based fine-tuning strategy to various protein language models, including masked PLMs and autoregressive family-based PLMs. Specifically, it introduces different scoring functions for these models and uses conditional ranking loss to fine-tune them. The experiments on fitness predic... | Rebuttal 1:
Rebuttal: Thank you very much for your review. Responses to your comments are given below. However, we highlight a number of misunderstanding and factual errors in the provided review. We kindly request that you re-evaluate your review in light of these clarifications. If we have sufficiently addressed your... | Summary: To train protein sequence to fitness regression models, it is attractive to fine tune protein language models (PLMs), as these have prior knowledge about the constraints underlying protein function, etc. The authors provide a specific fine tuning strategy, where the likelihood of a generative model is optimize... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed feedback - we appreciate the review. If we have sufficiently addressed your concerns, we kindly ask that you raise your score accordingly, or let us know if further clarification is needed.
Experimental Analysis
Clarification: We do provide a clear quantitat... | Summary: This paper examines likelihood-based / rank-based finetuning for pLM, particularly for the low data fitness prediction setting. The authors formalize pairwise ranking losses for masked models (e.g. ESM-series), family/MSA-based autoregressive models (e.g. PoET), and conditional models. The results show that th... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed feedback. If we have sufficiently addressed your concerns, we kindly ask that you raise your score accordingly, or let us know if further clarification is needed.
Claims and Evidence
Your comment is correct, Figure 1 does not show that. In fact, Table 13 (Ap... | null | null | null | null | null | null |
AutoStep: Locally adaptive involutive MCMC | Accept (poster) | Summary: This paper introduces AutoStep MCMC, a novel class of locally adaptive involutive MCMC methods that dynamically select the step size parameter at each iteration based on the local geometry of the target distribution. The proposed method extends previous adaptive MCMC techniques by integrating step size adapta... | Rebuttal 1:
Rebuttal: > Can AutoStep MCMC be extended to stochastic gradient MCMC like SGLD and SGHMC while preserving its key advantages?
Thanks, that’s a great point! Our focus in this paper is on exact, invariant MCMC methods, but it is indeed possible to extend AutoStep to SG MCMC. One approach would be to resampl... | Summary: The authors propose a method to tune the step size of MCMC algorithms so that, at each iteration, the acceptance rate is not too high (exploitation) or too low (exploration).
## update after rebuttal
I thank the authors for their clarification on a minor point that I raised. My overall assessment has not cha... | Rebuttal 1:
Rebuttal: Thank you for your time and insightful questions. We hope that our answer has addressed all your concerns.
> There is no visual evidence that tuning the step size helps the MCMC algorithm locate the target modes faster, and therefore converge faster.
Please note that locating modes is not a prim... | Summary: The paper proposes a MCMC method (called AutoStep MCMC) with locally adaptive step size selection. This method generalizes the previous involutive methods (e.g. RWMH, MALA, HMC etc) allowing adopting step size which is randomly drawn from some conditional distribution. The class of involutive MCMC methods is c... | Rebuttal 1:
Rebuttal: We are grateful for your thoughtful feedback. We are glad to address your questions one by one.
> Is it possible to provide numerical experiments with mixtures of distributions?
Thank you for the suggestion. Please first note that AutoStep is designed to help handle multiscale behaviour in targe... | Summary: This work proposes a framework for adaptive MCMC that enables sampling parameters to be optimized for the current location in the state space at each sampling step. In particular, the work focuses on adaptively adjusting the step size parameter which is found in common MCMC algorithms. The key challenge for su... | Rebuttal 1:
Rebuttal: Thank you for your insightful questions. We hope that our answer will address all your concerns.
> The proposed method does not achieve top performance on any specific scenario.
While AutoStep does not outperform all other methods across all examples, we would like to emphasize that it consiste... | null | null | null | null | null | null |
Evolving Minds: Logic-Informed Inference from Temporal Action Patterns | Accept (poster) | Summary: The paper introduces a single framework to infer human intentions, predict future actions, and interpretable logical rules. The motivation is that human actions occur irregularly and are driven by unobserved mental states/intentions. To address this, the paper proposes a framework combining the temporal point ... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer BKJo for the insightful analysis and recognition of our work! We hope our responses listed below can address your concerns.
**$\star$ Examples Revealing Newly Uncovered Rules**: We have reported a subset of temporal logic rules that have been identified as having real-... | Summary: This paper proposes combined logic-informed temporal point processes with amortized variational EM, allowing their method to infer underlying mental states reliably, even in low-data regimes.
Claims And Evidence: Their experimental results show the effectiveness of this framework on some synthetic as well as ... | Rebuttal 1:
Rebuttal: We thank Reviewer w7nu for the detailed analysis and insightful comments, which benefit us to further improve our paper! To address your concerns, we have prepared a detailed point-by-point response below.
**$\star$ Scalability**: Your comments are very valuable! Yes. We acknowledge that our orig... | Summary: The paper presents an amortized variational EM framework for understanding human mental states by modeling the relationship between actions and hidden mental events over time. Some innovations include using logic rules as priors to improve interpretability and approximating the posterior distribution of latent... | Rebuttal 1:
Rebuttal: Thank you for your positive feedback!
Regarding your question:
_"One question regarding autonomous rule learning for data-rich domains: in Table 17, the elements of temporal logic rules, such as PickUp and WantToPickUp, are still predefined, right?"_
Yes, in our current framework, predicates (i... | null | null | null | null | null | null | null | null |
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo | Accept (poster) | Summary: ## Summary
* This paper is based on previous work of solving diffusion inverse problems using sequential monte carlo [Practical and Asymptotically Exact Conditional Sampling in Diffusion Models]. More specifically, it takes the inner loop part of decoupled posterior sampling [Improving Diffusion Inverse Proble... | Rebuttal 1:
Rebuttal: Thank you for the comments, which have been of great value to improve the paper.
## Design choices in SMC make big difference in practice, motivating us to construct a new and better algorithm
The reviewer correctly points out that TDS and MCGDiff already enjoy asymptotic exactness. However, the ... | Summary: The paper proposes a new SMC method for sampling from the posterior of a Bayesian inverse problem that uses the as prior the time zero marginal of a learned score-based (or diffusion) generative model. The proposed SMC is influenced by the [1] but restricts itself to a Gaussian linear likelihood and instead of... | Rebuttal 1:
Rebuttal: Thank you for taking the time to read and comment on our paper, which certainly has been useful to make the paper better. We answer concerns and questions below.
## Assumptions in proposition A.1 concerns DAPS vs standard prior, not asymptotical exactness of DDSMC
Thanks for pointing out the uncla... | Summary: The authors consider solving linear inverse problems with diffusion models, but only those where the forward model has a tractable SVD. They build on the recent decoupled annealed posterior sampling (DAPS) method by Zhang (2024) by replacing its Langevin sampling inner-loop with a sequential Monte-Carlo (SMC)... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and we address their concerns below (see answer to zGWq regarding complexity and NFEs).
## We will clarify the asymptotical exactness guarantees
SMC provides consistent approximations of its sequence of targets $\lbrace \pi_t(x_{t:T})\rbrace_{t=0}^T$ unde... | Summary: The paper introduces Decoupled Diffusion Sequential Monte Carlo (DDSMC), a method for Bayesian inverse problems using diffusion priors. Main contributions include: Leveraging a modified diffusion process ("DAPS prior") to enable larger updates during sampling, improving exploration. Combining SMC with diffusio... | Rebuttal 1:
Rebuttal: We thank the reviewer for taking the time to comment on our paper. We have tried to address all your comments below, but there were a few points that we did not quite understand, so we kindly ask you to clarify if we in fact misunderstood some of your points.
## Merging SMC with decoupled diffusi... | null | null | null | null | null | null |
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