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Understanding Chain-of-Thought in LLMs through Information Theory | Accept (poster) | Summary: The paper introduces an information-theoretic framework to evaluate CoT reasoning in LLMs, quantifying "information gain" at each reasoning step to more accurately assess model performance without requiring annotated data. The approach outperforms existing outcome-based methods in identifying failure modes and... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for their time and feedback on our paper. Here below, we discuss the thought-provoking questions raised by the reviewer.
>The proposed paradigm seems challenging when attempting to explain existing R1-like work, particularly in the context of hand... | Summary: This paper introduces an information-theoretic framework to evaluate the quality of Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) without relying on annotated intermediate steps. Their framework quantifies the "information-gain" (IG) at each reasoning step, measuring how much each sub-task c... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful questions and feedback. Below, we address the concerns raised:
> Scalability/Generalization: Additional training required of a supervisor model, training details and construction of new dataset
**Training and Data Setup**: Our method requires additional... | Summary: This paper proposes a novel information-theoretic approach to evaluate Chain-of-Thought (CoT) reasoning in LLMs without annotated intermediate steps. The proposed framework can identify erroneous reasoning across diverse settings and consistently outperforms baselines.
Claims And Evidence: The statements are... | Rebuttal 1:
Rebuttal: First of all, we would like to thank the reviewer for their time and constructive comments to improve our paper. Here below we clarify all the questions raised by the reviewer.
> The empirical evaluations and adaptability to diverse CoT structures
Our framework evaluates each reasoning step using... | null | null | null | null | null | null | null | null |
Feature Importance Metrics in the Presence of Missing Data | Accept (poster) | Summary: This paper tackles the challenge of determining feature importance in realistic scenarios where data is missing. It is the first to explicitly formulate this problem and in doing so, introduces FMIG, a novel gradient-based metric that quantifies how small increases in the frequency of feature measurement can i... | Rebuttal 1:
Rebuttal: We greatly appreciate the reviewer's thoughtful review and positive feedback. We recognize the value of conducting additional experiments, particularly those utilizing datasets with established prior knowledge of expected effects, to further validate our results. Unfortunately, we do not have acce... | Summary: ## update after rebuttal: 2 --> 3
When applying feature importance (FI) methods as explanation techniques for machine learning (ML) models, the presence of missing data is typically not considered. The authors highlight the issue that missing values can impact FI method results and demonstrate this using the... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their detailed feedback, which has helped us to improve our paper.
## Methods and Evaluation Criteria:
For clarification on estimation, imputation, and the definition of bias, please refer to our response to reviewer rtga. To address the question about a distinc... | Summary: This paper introduces a conceptual framework that distinguishes between feature importance methods under missing data: (1) full-data feature importance evaluates each feature's importance if all feature values were present; (2) observed-data feature importance evaluates each feature's importance based on the a... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's positive feedback and valuable suggestions for improvement. Please find our responses below.
## Claims and Evidence
### Definition of Bias
We define $\theta$ as an estimand and $\hat{\theta}$ as its estimator. The bias of the estimator is given by:
\begin{equation}
... | null | null | null | null | null | null | null | null |
EmoGrowth: Incremental Multi-label Emotion Decoding with Augmented Emotional Relation Graph | Accept (poster) | Summary: The author proposes an Augmented Emotional Semantic Learning (AESL) framework, which incorporates an Emotion Relation Graph (ERG) to enhance emotion classification. To address the issue of missing partial labels in past data, a reliable soft label generation method is introduced. Additionally, a Relation-based... | Rebuttal 1:
Rebuttal: **1.I would like to understand the practical significance of the Multi-Label Class Incremental Emotion Decoding field. Many existing emotion-related methods are capable of integrating different tasks into a single input for large models or other frameworks.**
(1) In real world Brain-Computer Int... | Summary: This paper introduces multi-label fine-grained class incremental emotion decoding, which aims to develop models capable of incrementally learning new emotion categories while maintaining the ability to recognize multiple concurrent emotions. It proposes an Augmented Emotional Semantics Learning (AESL) framewor... | Rebuttal 1:
Rebuttal: **1.What are the unique challenges of multi-label class incremental emotion decoding compared to similar problems?**
(1) Unlike traditional single-label class incremental learning, multi-label class incremental faces unique challenges in addressing catastrophic forgetting from past- and future-mi... | Summary: The paper introduces multi-label, fine-grained class incremental emotion decoding AESL to adapt to the scenarios where novel emotion categories continuously emerge. To solve the critical past-missing partial label problem, AESL introduces an augmented Emotional Relation Graph (ERG) module, using graph-based la... | Rebuttal 1:
Rebuttal: **1.The motivation of the task.**
The practical significance of multi-label incremental emotion decoding can be summarized as two folds.
(1) In real world Brain-Computer Interfaces, practical applications often require dynamic adaptation to new emotion categories over time. For example, in clini... | Summary: The paper proposes **EmoGrowth**, a framework addressing **multi-label fine-grained class incremental emotion decoding**. This paradigm enables models to learn **new emotion categories incrementally** while preserving the ability to recognize **multiple concurrent emotions** in dynamic real-world environments.... | Rebuttal 1:
Rebuttal: **1.Conduct ablation studies on emotion order and task size variability.**
For the emotion order, we conducted 10 randomized shuffles of the category sequence. The experimental results are presented below:
|Method | Brain27 B0-I9 | Brain27 B0-I3 | Brain27 B15-I3| Brain27 B15-I2 |
|-----|---------... | null | null | null | null | null | null |
It's Not Just a Phase: On Investigating Phase Transitions in Deep Learning-based Side-channel Analysis | Reject | Summary: This paper investigates deep learning-based side-channel analysis and introduces mechanistic interpretability methods to understand how neural networks trained on side-channel models learn. Specifically, the paper transforms black-box evaluation into white-box evaluation through reverse engineering, revealing ... | Rebuttal 1:
Rebuttal: Thank you for the review. We are glad the motivations and phenomena we describe are clear.
W1: As mentioned in the paper, the (potential) reasons for learning occurring in discrete phase transitions are initially discussed in [1], offering preliminary insights into the phenomenon. More elaborate... | Summary: The paper explores the novel concept of phase transitions within the context of Deep Learning-based Side-channel Analysis (DLSCA). It introduces an approach for mechanistic interpretability, aimed at understanding the detailed mechanisms of how deep learning models adapt and operate during the phase transition... | Rebuttal 1:
Rebuttal: Thank you for the positive review. We are glad you found the analyses both deep and accessible.
W1: While this might be true, we hope this initial work will simplify future analyses by enumerating some (potentially) common structures. Additionally, automating some of these analyses could be pos... | Summary: The paper applies mechanistic interpretability techniques to side-channel analysis, which is used to extract secret keys from protected devices by monitoring physical factors like power consumption. The authors investigate model behavior during phase transitions (sudden jumps in accuracy) by analyzing activati... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We are pleased you think DLSCA might be a valuable testbed for future MI research.
W1: Improving the security of devices requires a good understanding of the devices' vulnerabilities, and this work provides a concrete approach to understanding how and why a particular ... | Summary: This paper investigates the feasibility of applying Mechanistic Interpretability (MI) to deep learning-based side-channel analysis (DLSCA) to enhance the interpretability of deep neural networks in security evaluations. The authors explore how neural networks exploit side-channel leakage and identify learned s... | Rebuttal 1:
Rebuttal: Thank you for the feedback. We are glad to hear the core concepts were understandable, even without extensive prior knowledge of MI or DLSCA.
Q1: The principles of MI can indeed be extended to other security-related tasks, but the specific analysis techniques and challenges can vary significantly... | null | null | null | null | null | null |
DPO Meets PPO: Reinforced Token Optimization for RLHF | Accept (spotlight poster) | Summary: This paper develops an RLHF framework with a fine-grained token-wise reward characterization. Specifically, they model RLHF as an MDP, offering a more precise token-wise characterization of the LLM’s generation process. They introduce RTO algorithm, which extracts token-wise reward signals from offline prefere... | Rebuttal 1:
Rebuttal: Thank you for your review and support. Below are our response to your questions.
**Q1:** Assumption 3.1 is not a rigorous assumption. The parameters $A$ and
$\xi$ have not been defined. What are their possible ranges of values? The current statement is not an assumption without conditions $A$ an... | Summary: Summary: The authors propose a token-level MDP formulation for LLM. post-training. They use the token-level action probabilities from a Direct Preference Optimization (DPO) trained LLM. Authors argue that the current formulation of LLM post-training is closer to a contextual bandit than it is to a reinforcemen... | Rebuttal 1:
Rebuttal: Thank you for your review and support. Below are our response to your questions.
**Q1:** I have a question on the results. SimPO method outperforms PPO by a large margin (Table 1). Do you have any intuition for this?
**A1:** Our observation is that the RL-free methods are good at fitting the **... | Summary: The paper introduces Reinforced Token Optimization (RTO), a framework that integrates Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO) to improve Reinforcement Learning from Human Feedback (RLHF). The authors argue that existing RLHF implementations using PPO underperform due to a mi... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. Below are our responses.
**Question Regarding Theory:** Theoretical Proofs: Could you provide a complete proof for the sample efficiency claim in Appendix B?
**Response:** We have provided complete and rigorous proofs for both Proposition 3.2 and Theorem 4.2 in... | Summary: The paper presents RTO, a novel reward learning method for tuning LLMs from preference data. Such a process involves two stages: reward modeling and an RL step (typically PPO). The main novelty of this paper lies in modifying the Bradley-Terry (BT) loss in the reward modeling step to yield a reshaped token-le... | Rebuttal 1:
Rebuttal: Thank you for reviewing our paper. We summarize your questions and address them as follows.
**Q1:** MDP fomulation and Previous Theoretical Work
**A1:** We agree with the reviewer that the existing PPO is implemented in an *MDP with zero reward for all but the last token*, but we would like to ... | null | null | null | null | null | null |
Stochastic Online Conformal Prediction with Semi-Bandit Feedback | Accept (poster) | Summary: The paper studies the problem of conformal prediction with semi-bandit feedback. In this setting, it formulates an online learning problem where the algorithm must generate conformal sets that maintain valid coverage guarantees in each round. The authors propose an algorithm that satisfies this requirement and... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We address the questions and major concerns below:
**Concern 1: Line 285-287 Proof**
We apologize for the confusion. We will fix the typo in the revised draft. We also note that the final regret bound is correct because we add the additional $\e... | Summary: The paper introduces a novel algorithm for online conformal prediction in settings where only partial feedback is available, specifically semi-bandit feedback. Conformal prediction is a framework for uncertainty quantification that outputs prediction sets with guaranteed coverage probabilities. The key challen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We address the questions and major concerns below:
**Concern 1: Lemma 4.2**
We apologize for the confusion. A more detailed proof is included below:
*Base case:* when $t = 1$, we set $\tau_1 = -\infty$. Thus, $\tau_1 \le \tau^*$.
*Inductive H... | Summary: The paper proposes a novel stochastic online conformal prediction algorithm designed for constructing prediction sets under semi-bandit feedback. It addresses the challenge of sequential decision-making where feedback is limited to partial information (e.g., only observing certain bids or labels). The algorith... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful comments. We address the questions and major concerns below:
**Concern 1: Additional Comparative Analysis**
We appreciate the reviewer’s suggestion. In both our discussions and experiments, we have considered a comprehensive set of popular online confor... | Summary: This paper introduces an online conformal prediction method with semi-bandit feedback (i.e. we only observe the true label if it is contained in the prediction set). Authors show that, under the iid setting, their method controls the expected cumulative regret and that $\tau_t$ (the threholding value at time $... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful comments; we address major concerns below.
**Concern 1: Comparison to [1]**
By our understanding, the algorithm in [1] cannot be straightforwardly modified to handle semi-bandit feedback. Specifically, they use a strangeness measure $A_t:(z_1,...,z_t)\mapsto(\alpha_1... | null | null | null | null | null | null |
Improving Transformer World Models for Data-Efficient RL | Accept (poster) | Summary: This paper presents a technically sound and empirically robust contribution to MBRL, with clear innovations in tokenization and transformer training. While the Craftax-centric evaluation limits immediate generalizability, the methodological advancements (NNT, BTF) are likely to inspire follow-up work. The pape... | Rebuttal 1:
Rebuttal: Thank you for your positive evaluation of our work. We address your questions and comments below:
**Weaknesses **
(1) As detailed in our rebuttal to Reviewer fn71, we successfully trained an MBRL agent on the MinAtar benchmark, reusing our core MBRL components with little hyperparameter tuning. ... | Summary: This paper proposes an approach for model-based reinforcement learning (MBRL) to improve sample efficiency and performance on the challenging Craftax-classic benchmark.
The method includes three improvements for both policy and transformer world model (TWM), which are “Dyna with warmup”, “nearest neighbor toke... | Rebuttal 1:
Rebuttal: **Essential References Not Discussed:**
We thank you for pointing out the REM reference: we will include it in our revised paper. You are right that both block teacher forcing (BTF) and REM predict all the next frame tokens jointly. However, while REM uses a retentive network, BTF is applicable t... | Summary: The authors propose a number of improvements to dreamer-style MBRL to achieve SOTA performance on the Craftax benchmark.
Claims And Evidence: The authors mention three main contributions:
- They show that training on both real and imagined data is better than solely on imagined data
- They embed image patches... | Rebuttal 1:
Rebuttal: We are pleased that you enjoyed reading the paper and that you think that reaching SOTA on Craftax is a notable achievement, on a well-known and difficult benchmark.
**Claims and Evidence:**
(1) You are correct in that Atari with sticky actions (frameskip) makes it stochastic. We will remove ou... | Summary: The authors propose a new model-based RL method that achieves SOTA at Crafter. The superiority of there method stems from a variety of novel insights:
- adding a memory with low-dimensional hidden states and passing both the image embedding and the memory output to subsequent networks
- training on a mix of re... | Rebuttal 1:
Rebuttal: We appreciate your favorable comments regarding our work.
To further validate the robustness of our approach, we have conducted additional experiments on another grid-world environment MinAtar (https://github.com/kenjyoung/MinAtar), a set of four simplified Atari 2600 games. MinAtar contains symb... | null | null | null | null | null | null |
Deliberation in Latent Space via Differentiable Cache Augmentation | Accept (poster) | Summary: This work proposes a novel framework to augment an existing pretrained LLM with a differentiable cache module that can be finetuned on a set of data to improve performance of the model when its combined with aforementioned cache extender. Authors claim that the novel design of the module and look-ahead trainin... | Rebuttal 1:
Rebuttal: Dear Reviewer vZqn,
We sincerely thank you for your detailed review and insightful comments on our work (Submission 403). We appreciate you acknowledging the merit of our core idea, the quality of the related work section, and the potential for future work stemming from our method.
We understand... | Summary: This paper proposes a novel method that augment the memories (kv-caches) with a set of latent embeddings from auxiliary compressor modules. This offers two main advantages, end-to-end differentiability by using soft (continuous) tokens and asynchronous operation by using compressor in offline while freezing th... | Rebuttal 1:
Rebuttal: Dear Reviewer 9maT,
Thank you for the positive evaluation, thoughtful review, and examination of the supplement. We appreciate your constructive questions and address them below:
### **Regarding Training Speed/Process:**
You asked about the training speed and the forward passes involved.
**Our... | Summary: In this work, the authors train a hyper-network (termed “coprocessor”) which takes a KV-cache from a language model mid-generation and produces a set latent embeddings which are appended to the KV-cache before producing the final answer. Critically, the coprocessor produces the latent embeddings with *a single... | Rebuttal 1:
Rebuttal: Dear Reviewer w2nr,
Thank you for the detailed review of Submission 403 and the constructive feedback. We're glad you found the method novel and the related work discussion good. We address your points below:
### **Regarding Claim 1 (Perplexity Reduction & Baseline Fairness):**
You questioned t... | Summary: In this work a co-processor is trained to get as input the generated KV-cache of a frozen model - after given an input x and a set of soft tokens and produce a set of latent embeddings z. These embeddings are appended to the KV-cache (augmentation) and the original frozen model decodes towards output y (gener... | Rebuttal 1:
Rebuttal: Dear Reviewer n6p1,
Thank you very much for your positive evaluation and your insightful review of our work. We greatly appreciate your accurate summary, positive feedback on our claims and experiments, and for reviewing the supplementary material thoroughly. We are encouraged that you see this a... | null | null | null | null | null | null |
Catch Your Emotion: Sharpening Emotion Perception in Multimodal Large Language Models | Accept (spotlight poster) | Summary: This paper proposes a method called Sharpening Emotion Perception in Multimodal Large Language Models (SEPM) to improve emotion recognition in MLLMs. It addresses two challenges: confusion between semantically similar emotions and the visual redundancy that distracts from emotional cues. SEPM incorporates a tw... | Rebuttal 1:
Rebuttal: Dear Reviewer W74m:
We are deeply grateful for your positive feedback on our work and the insightful suggestions. We have carefully reviewed each point and provided detailed responses accordingly.
**Q1: Performance under different prompts** (Other Strengths And Weaknesses)
To explore the perfo... | Summary: This paper proposes SEPM to tackle emotion recognition challenges in multimodal models. It focuses on issues like confusing similar emotions and visual noise. SEPM introduces a two-stage inference process: a coarse-to-fine approach to improve confidence in emotion classification and a focus on relevant emotion... | Rebuttal 1:
Rebuttal: Dear Reviewer 6rbK:
Thank you very much for your valuable comments and constructive feedback. Below, we carefully respond to each of your concerns point-by-point, providing detailed explanations and supplementary evidence to further clarify our approach and demonstrate its effectiveness.
**Q1: ... | Summary: This paper presents Sharpening Emotion Perception in MLLMs (SEPM), a training-free method to enhance emotional reasoning in multimodal large language models. SEPM improves emotion classification by using a Confidence-Guided Inference framework and Focus-on-Emotion Visual Augmentation to reduce distractions. Ex... | Rebuttal 1:
Rebuttal: Dear Reviewer jWJJ:
Thank you again for your thoughtful and constructive suggestions. In the following responses, we address each of your points thoroughly, providing additional explanations and supporting evidence to strengthen the clarity of our methods.
**Q1: Discussion on generalizability of... | Summary: This paper proposes a training-free approach for emotion classification using Multimodal Large Language Models (MLLMs). They find that MLLMs (1) struggles to distinguish between semantically similar emotions, and (2) are overwhelmed by redundant visual information. To address these challenges, they propose a C... | Rebuttal 1:
Rebuttal: Dear Reviewer wn8T:
We sincerely appreciate your time and effort in reviewing our paper. We have provided further clarification on each of the issues you raised. We hope the detailed responses below fully address your concerns, and we would be grateful if you would consider updating your score.
... | null | null | null | null | null | null |
ExLM: Rethinking the Impact of $\texttt{[MASK]}$ Tokens in Masked Language Models | Accept (poster) | Summary: This paper investigates the role of mask tokens in Masked Language Models (MLMs). The authors first provide an empirical examination of the effect of the mask token through two perspectives: corrupted tokens and unreal tokens. Additionally, the authors propose a new algorithm, EXLM, to further enhance performa... | Rebuttal 1:
Rebuttal: We appreciate the insightful suggestions from Reviewer j1MR. In the following sections, we will address all your concerns. These discussions will also be incorporated into the final camera-ready version of the paper. Any further comments are welcome!
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**Q1: What is the authors' main contrib... | Summary: This paper studies the semantic corruption issue in masked language modeling (MLM). To start the research, the authors design an experiment (repeated MLM) to show the relationship and significance of the corrupted semantics caused by masking. As a result, ExLM is proposed as a solution for the problem. In this... | Rebuttal 1:
Rebuttal: We express our gratitude to Reviewer 3GXq for the suggestions. In the following, we address all your concerns regarding the evaluation of NLU tasks, additional experiments on more tasks, the efficiency analysis of ExLM, and the details of repeated MLM experiments. These discussions will also be in... | Summary: This paper gives a new way of utilizing [MASK] tokens in masked language models. It first performs an analysis of the semantic aspects of the [MASK] token and then proposes ExLM wherein multiple [MASK] tokens are introduced during pre-training. The authors then propose to utilize a learnt transition to get mul... | Rebuttal 1:
Rebuttal: Thank you for your valuable suggestions. In the following, we provide detailed responses to all your concerns regarding the missing reference, model details, writing organization, and implementation issues. These discussions will also be incorporated into the final camera-ready version of our pape... | Summary: This work presents a deeper analysis into the effectiveness of mask token in MLM pre-training.
The authors argue that the conventional use of [MASK] tokens can lead to a "corrupted semantics problem" where the masked context may become ambiguous and lead to multiple interpretations.
To highlight this issue, th... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive feedback. We sincerely appreciate your valuable suggestions. Below, we address all your concerns regarding the SMILES tasks, the standard deviation of model performance, and additional NLU tasks. The relevant results will be incorporated into the fina... | null | null | null | null | null | null |
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation | Accept (spotlight poster) | Summary: The authors propose a new method to impute missing data in tabular data sets. This method, named CACTI, incorporate three components : a mask autoencoding approach, a median truncated copy-masking training strategy and the use of semantic relations between features.
The proposed method is evaluated across 10 ... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for the helpful feedback and questions.
## Comments Addressed
We've added CMAE (CACTI without context) as an additional benchmark in the main results table (qrHN rebuttal Table. R1). We’ve also added a comparison between CACTI, CMAE to quantify the statistical... | Summary: This paper addresses the missing data imputation problem by using a transformer-based architecture that leverages the missing patterns and textual information about features as inductive biases to improve imputation accuracy. Specifically, the paper proposes a median truncated copy masking training strategy th... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful review and suggestions. We would like to highlight that all the individual per dataset/missingness condition results for all analyses performed in this paper were previously reported with 95% confidence intervals in the Appendix. We choose to exclude stan... | Summary: The authors introduce CACTI (Context Aware Copy masked Tabular Imputation) for imputing missing values in tabular data. CACTI’s backbone is a Masked Autoencoder based on Transformers. It brings the following key modifications to this architecture:
- Instead of randomly masking observed values as in ReMasker, ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback.
## Does context help?
Our ablation analysis focused on the *relative* contributions of median truncated copy masking (MT-CM) and context awareness with fixed architecture settings and hyperparameters (Appendix C.2). Random masking autoencoder (RM... | null | null | null | null | null | null | null | null |
BSLoRA: Enhancing the Parameter Efficiency of LoRA with Intra-Layer and Inter-Layer Sharing | Accept (poster) | Summary: This paper proposes Bi-Share LoRA (BSLoRA), a parameter-efficient fine-tuning approach for large language models (LLMs). The key idea is to improve upon standard Low-Rank Adaptation (LoRA) by introducing intra-layer and inter-layer parameter sharing to reduce the number of trainable parameters while maintainin... | Rebuttal 1:
Rebuttal: **Comment:** "*Limited empirical improvements: The performance gain is marginal, making it unclear whether the complexity trade-off is justified.*"
**Answer to C1**: Thank you for your comment. Please refer to R2C4.
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**Comment:** "*No throughput/memory efficiency evaluation: The paper claim... | Summary: The paper introduces Bi-Share LoRA (BSLoRA), which improves memory efficiency and inference speed by sharing parameters both within a layer (intra-layer) and across layers (inter-layer). The approach also introduces three shape transformation techniques—Slice Sharing, Gate Transformation, and Kronecker Extensi... | Rebuttal 1:
Rebuttal: **Comment:** "*... settings for Table 1 and Table 2 are not consistent. The ablation study evaluates the effectiveness of the three different modules by removing each component sequentially*."
**Answer of C1**: Thank you for your observation. The settings in Tab 1 and Tab 2 were designed to eva... | Summary: This paper introduce a method, sharing lora parameters across local, intra-layer, inter-layer. To address the shape mismatch issues, shape transformation are introduced including slice sharding, gate transformation, KRONECKEREXTENSION. Results on different datasets show the effectiveness of the method.
Claims... | Rebuttal 1:
Rebuttal: **Comment:** "*Fairness of Comparison with Baselines: The comparison between BSLoRA and baselines such as ShareLoRA and Tied LoRA may be unfair. ... To evaluate the true effectiveness of BS LoRA’s design, how does applying LoRA to both attention and MLP modules compare to applying it to only one m... | Summary: In this paper, the authors proposed BSLoRA that add intro-layer and inter-layer sharing for LoRA to reduce trainable parameters while maintenance the performance. Multiple experiments were conducted on several benchmark datasets and showed slightly better performance.
Claims And Evidence: Yes.
Methods And Ev... | Rebuttal 1:
Rebuttal: **Comment:** "*Given that LoRA already reduced the trainable parameters dramatically compared to full training, the additional reduction from the proposed intra-layer and inter-layer sharing is relatively small.*"
**Answer to C1**: Thank you for your insightful comment. While it is true that LoRA... | null | null | null | null | null | null |
LLMs on the Line: Data Determines Loss-to-Loss Scaling Laws | Accept (poster) | Summary: The paper focuses on loss-to-loss scaling laws in large language models (LLMs), which relate losses across pretraining datasets and downstream tasks. The author finds that 1. loss-to-loss scaling consistently follows shifted power-law trends, enabling prediction of test performance from training loss, as detai... | Rebuttal 1:
Rebuttal: Thanks for your helpful comments and feedback.
**[EDA1 - Conclusions are subjective]**: We now quantify our findings in two ways:
1. We quantify the goodness of fit of the loss-to-loss power laws as $R^2$. We show this in our revised [Fig. 1](https://ibb.co/kgpRZNZP) (note that Fig. 1 has also re... | Summary: The paper studies loss-to-loss scaling laws in language models, covering both predicting the language modeling log loss across different data distributions (“train-to-train”) and predicting log loss proxies for downstream tasks performance (“train-to-test’). The main finding in this paper is that loss-to-loss ... | Rebuttal 1:
Rebuttal: We thank you for your helpful comments and feedback. We address each of your questions and concerns below.
**[Q1 - Models trained and evaluated and complete results]**: Thank you for noticing this; we have made amendments in multiple places: First, we now mention in Sec. 4 the size of not only ou... | Summary: This paper explores how loss-to-loss scaling laws depend on various factors in the training setting. While compute-to-loss scaling laws are often studied (i.e., training on X tokens, Y parameters will give you Z loss), there is recent interest in loss-to-loss scaling laws, which show how evaluation/training on... | Rebuttal 1:
Rebuttal: Thank you for your helpful comments and feedback. We address your concerns below:
**[Q1, Q3, OSW Clarity 1 - On the construction and fitting of scaling laws]**: We have added two Appendix sections that (1) detail the scaling law formulation and (2) explain how parameters are estimated. Here’s a ... | Summary: The paper investigates how loss-to-loss scaling (i.e. scaling laws between losses on different datasets) for LLMs is influenced by model architecture, tokenizer, and training datasets. The authors experimentally find that:
1. loss-to-loss scaling consistently follows shifted power laws.
2. The effects of pret... | Rebuttal 1:
Rebuttal: We thank you for the helpful feedback. We address your concerns as follows:
**[W1 & Q2a - On comparing raw numbers]**: Comparing individual performance metrics like MMLU is indeed possible and can illustrate the effectiveness of an intervention for a specific model scale and setting. For example,... | null | null | null | null | null | null |
Attention-Only Transformers via Unrolled Subspace Denoising | Accept (poster) | Summary: This paper presents a new transformer architecture by interpreting each layer as a subspace denoising operator for the token representations. The work is interesting, and the paper is well written in general. The authors also provide theoretical analysis on the developed model. Experimental results show that t... | Rebuttal 1:
Rebuttal: > **Q1.** More fair discussions should be given in the experiments. For instance, the results in Table 1 show that the proposed method is 9.3% lower than the compared baseline. Using "comparable performance" is unfair.
**A1.** Thanks for the comment. To address the concern, we will change "compar... | Summary: The authors propose an attention-only architecture, using the multi-head subspace self-attention (MSSA), first proposed by Yu et al., NeurIPS 2023 ( and also JMLR 2024). They have a model in which the embeddings of the tokens come from a mixture of different subspaces, albeit with additional additive noise mak... | Rebuttal 1:
Rebuttal: > **Q1.** **Inconsistent notation**: $Z^{(l)}$, $f^l$ have layer superscript $l$ in the equation but $U_k$ never does, giving the impression that $U_k$ are the same for each layer. Figure 3 architecture has $U^l$ and so does Yu et al., 2023.
**A1.** Thanks for pointing this out. This confusion s... | Summary: This paper proposes an attention-only transformer (AoT) architecture that eliminates the feed-forward network (FFN) modules found in traditional transformers, including CRAFT's Multi-head Subspace Self-Attention (MSSA). The authors argue that representation learning should compress noisy token representations ... | Rebuttal 1:
Rebuttal: > **Weakness 1.** **Inconsistency between theory and implementation**: According to the parameter calculations, MSSA ultimately employs the same methodology as MHSA, where all projection matrices $U_o$, $U_q$, $U_k$, and $U_v$ are not shared...
**A1.** We should clarify that the theory and implem... | null | null | null | null | null | null | null | null |
Learngene Tells You How to Customize: Task-Aware Parameter Initialization at Flexible Scales | Accept (poster) | Summary: This paper proposes a novel parameter initialization method called TAL, aiming to enhance the initialization effect of models for different tasks. Building upon the previous GHN and Learngene frameworks, TAL addresses their limitations. Although the GHN method is effective, it performs inadequately when dealin... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
### Q1
The author should elaborate on the learngene theory and the computational graph in more detail.
### A1
We elaborate on the concept of Learngene in the introduction and related work of the paper. The essence of the Learngen... | Summary: This paper addresses the high computational and storage overheads involved in training large pretrained models by focusing on effective parameter initialization. Building on recent advances in Graph HyperNetworks (GHN) and the Learngene framework, the authors propose a novel method called Task-Aware Learngene ... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
### Q1
An important Task-Aware Parameter initialization baseline is not compared: Learning...(TIP 2024). The models used in this paper is too small and shallow.
### A1
Thank you for pointing this out. We will cite it in the rele... | Summary: Authors propose TAL, an encoder-decoder method to generate parameters for initializing models of various sizes given a model architecture and a task embedding.
Claims And Evidence: 1. Yes
Methods And Evaluation Criteria: 1. The method relies on a single ancestry model, which might be problematic as the lates... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
### Q1
The method relies on a single ancestry model, which might be problematic as the latest model might serve as a better ancestry model, and then requires retraining. And since the entire framework requires a certain level of... | Summary: This paper presents Task-Aware Learngene (TAL), designed to initialize large models via parameter prediction. To accomplish this, the authors first employ an encoder-decoder architecture for the TAL model and train it under the supervision of an ancestry model to facilitate knowledge transfer. Subsequently, wi... | Rebuttal 1:
Rebuttal: We thank you for your reviews and address your concerns as follows.
### Q1
Were all methods trained on the same dataset? If so, why does only TAL+ perform well? If not, the comparison is unfair, undermining the claim's validity.
### A1
TAL and GHN-3, LoGAH do use the same model training dataset, ... | null | null | null | null | null | null |
Self-Supervised Transformers as Iterative Solution Improvers for Constraint Satisfaction | Accept (poster) | Summary: This paper mainly focuses on constraint satisfaction problems (CSPs). It proposes a transformer-based model to serve as an iterative solution improver, repeatedly revising the generated CSP solution until it is correct.
- The main _idea_ is to leverage the self-supervised learning paradigm, that is, to train ... | Rebuttal 1:
Rebuttal: We appreciate your thoughtful comments and are glad that you see the potential for our method in aiding the development of large-scale CSP solving.
>no discussion about RL-based methods both in theoretical analysis and experimental comparison.
We would like to clarify that both theoretical and ... | Summary: This paper presents a Transformer-based learning framework for solving CSPs. Specifically, they leverage a transformer architecture to refine the solution, where they show that decision variable position encoding is key for transformer learning. They adopt a continuous approximation as loss function, and show ... | Rebuttal 1:
Rebuttal: Thank you for the thoughtful feedback and questions. We’ve conducted new experiments and clarified key aspects of the method in response to your concerns, which we believe strengthen the submission significantly.
> Can the authors benchmark on more complicated problems?
We have added experiments... | Summary: The paper introduces an iterative, local search approach towards solving constraint satisfaction problems with transformer models and self-supervision.
A new encoding scheme and the self-supervised training scheme are introduced plus a differentiable approximation for the violation of key constraints. The main... | Rebuttal 1:
Rebuttal: We sincerely appreciate the encouraging feedback and thoughtful comments.
> self-supervision is more limited because it needs support for every constraint to be differentiable.
You're absolutely right to point out that a key limitation of the self-supervised approach is the need to define differ... | Summary: The paper proposes training a transformer architecture in a self-supervised manner to solve constraint satisfaction problems. The input to the model is an assignment of values to the variables and the output is a refined assignment. The assignment is encoded as tokens and the constraints of the problem are enc... | Rebuttal 1:
Rebuttal: We greatly appreciate your thorough feedback and have conducted new experiments which we believe have substantially improved the paper.
> Additional benchmark.
We have adapted ConsFormer for MAXCUT based on your suggestion.
- MAXCUT is the problem of partitioning nodes of a graph into two sets... | null | null | null | null | null | null |
FlipAttack: Jailbreak LLMs via Flipping | Accept (poster) | Summary: The authors studied a simple yet effective jailbreak method to attack recent state-of-the-art LLMs in one query. They exploit the auto-regression of LLMs and introduce the left-side perturbation to the text. Four flipping methods are proposed to disguise the harmful content and fool the LLMs. The proposed meth... | Rebuttal 1:
Rebuttal: Thanks for your insightful and constructive review. We response to each question as follows. Following your suggestion, **all modifications will be added to the final paper.**
**Inconsistent Conclusions**
- For different LLMs, **their abilities are different**, which may lead to different conclu... | Summary: The paper introduces FlipAttack, a novel jailbreak attack method designed for black-box large language models (LLMs). The authors analyze the autoregressive nature of LLMs, revealing that they struggle to comprehend text when perturbations are placed on the left side. Based on this insight, they propose a meth... | Rebuttal 1:
Rebuttal: Thanks for your insightful and constructive review. We response to each question as follows. Following your suggestion, **all modifications will be added to the final paper.**
**Theoretical Analyses**
- Jailbreak attack is a **practical direction**.
- We provide **empirical analyses** to demonst... | Summary: The authors propose FlipAttack, which encodes malicious prompts by reordering words or characters and relies on the reasoning capabilities of the LLM s.t. it can decipher the prompt. The authors demonstrate empirically that this procedure often bypasses the guardrails, is very efficient, and is difficult to de... | Rebuttal 1:
Rebuttal: Thanks for your insightful and constructive review. We response to each question as follows. Following your suggestion, **all modifications will be added to the final paper.**
**Left-side Perturbation**
- Left-side perturbation is the **principle idea** of our proposed FlipAttack. It merely helps... | null | null | null | null | null | null | null | null |
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages | Accept (poster) | Summary: This paper introduces Foundation Molecular Grammar (FMG). This approach uses a multimodal large language model to identify meaningful substructures in molecular graphs, generating an interpretable grammar for molecule generation. By rendering molecules as images and prompting the model with specialized prompts... | Rebuttal 1:
Rebuttal: *The authors emphasize interpretability as a major advantage of using FMG. However, the paper only provides limited examples.... A deeper discussion or demonstration (e.g., a domain-expert walkthrough...) would reinforce the claim that this approach is uniquely transparent or insightful.*
* The e... | Summary: In this paper, the authors show that one can incorporate the “graph grammar” of molecules into a multimodal language model. Essentially, the method, called FMG, (i) takes a molecular graph as an input, (ii) extracts “features” of such a graph, (iii) represent them with images, (iv) ask a multimodal (vision-lan... | Rebuttal 1:
Rebuttal: *The claims are reasonable and the method itself is well-justified, albeit quite complex, involving many building blocks. I would like to see an ablation study on each building block.*
* Thanks for acknowledging the reasonableness of our method! We do have an ablation study on each building block... | Summary: The paper proposes Foundation Molecular Grammar (FMG) using multi-modal foundation models. FMG induces interpretable graph grammars by converting molecules to images and using LLMs to identify the connection between molecular substructures. It outperforms baselines in molecular generation benchmarks, excelling... | Rebuttal 1:
Rebuttal: *The paper uses LLMs to replace heuristics-based rules in molecular grammar methods, which is a novel and interesting way to incorporate domain knowledge into the process of molecular grammar learning.*
Thanks for recognizing the novelty, soundness, and intrigue of our work!
*Why did the authors... | null | null | null | null | null | null | null | null |
Unifews: You Need Fewer Operations for Efficient Graph Neural Networks | Accept (poster) | Summary: This paper proposes Unifews, a sparsification for both graph and weight matrix. The purpose of such sparsification is to boost the scalability of GNN.
Claims And Evidence: A major claim of this paper is the speed-up on the Ogbn-papers100m dataset, I have some questions about this claim, please refer to latter... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback. We have carefully checked the experiments and would like to address the questions as follows.
## W1&Q4
We further evaluate other scalability methods and present representative accuracy and training time as below. We also evaluate the smaller `co... | Summary: This paper introduces UNIFEWS a technique to sparsify GNNs by dropping messages. The authors justify their UNIFEW by proofs that provide theoretical guarantees. In experiments, UNIFEWS allows dropping almost all edges in the graphs without much impact on predictive performance. This leads to a significant spee... | Rebuttal 1:
Rebuttal: We are thankful for the detailed and insightful comments from the reviewer. We address the specific reviews below with further evaluation results.
## W1
In the paper, we do not show the wall-clock time for iterative models mainly because of the **variety in baseline implementations**. The existin... | Summary: This paper explores strategies to accelerate GNN computation by integrating both structural sparsification and weight parameter pruning. Specifically, it introduces a framework called UNIFEWS, which adaptively and progressively simplifies computations while providing theoretical guarantees on the accuracy trad... | Rebuttal 1:
Rebuttal: We sincerely appreciate the constructive feedback from the reviewer. Below, we provide detailed responses with new experiments following the suggestions.
## T1
The range of the constants in *Thm 4.1* is discussed in *Sec B.2*. The constant $2<\alpha<3$ represents degree distribution, and $\sigma>... | Summary: The paper proposes a framework named UNIFEWS (UNIFied Entry-Wise Sparsification), which aims to improve the learning efficiency of Graph Neural Networks (GNNs) by jointly sparsifying the graph and the weight matrix. By incrementally increasing sparsity layer by layer, the framework significantly reduces the co... | Rebuttal 1:
Rebuttal: We are thankful to the reviewer for recognizing our theoretical and experimental contributions. We respectfully address the specific reviews below.
## W1
**Power-law** for degree distributions is frequently observed in real-world graphs, especially large-scale ones focused on in this study, such ... | null | null | null | null | null | null |
PCEvolve: Private Contrastive Evolution for Synthetic Dataset Generation via Few-Shot Private Data and Generative APIs | Accept (spotlight poster) | Summary: The paper addresses the problem of generating Differentially Private (DP) synthetic images using APIs, focusing on the setting in which only a small number of private data samples are available.
The authors observe that a popular prior work, Private Evolution, struggles in few-shot private data scenarios ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our work! Below, we provide responses to address your concerns and suggestions, with *Lines xxx* and *Section x* referring to specific parts of our paper. We hope these clarifications effectively resolve your concerns, and we also thank you... | Summary: This paper proposes Privacy Contrastive Evolution (PCE), a new algorithm for generating high-quality differentially private (DP) synthetic images from small amounts of private data using a generative API. PCE addresses the limitations of existing Privacy Evolution (PE) algorithms, which struggle with high nois... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our work! Below, we provide responses to address your concerns, with *Lines xxx* and *Section x* referring to specific parts of our paper. We hope these clarifications effectively resolve your concerns.
**Concern 1: Generalization to Broad... | Summary: This paper introduces a new method for generating synthetic images under differential privacy called Private Contrastive Evolution (PCE).
This method is designed for the case of few-shot private data, which is prevalent in healthcare, using a generative model behind an API.
PCE works by initializing a syntheti... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our work! Below, we provide responses to address your concerns and suggestions for extensions, with *Lines xxx* and *Section x* referring to specific parts of our paper. We hope these clarifications effectively resolve your concerns. We als... | Summary: The authors present an interesting approach to an API-assisted algorithm called Private Contrastive Evolution (PCE) to address the challenge of generating high-quality differentially private (DP) synthetic images from few-shot private data using generative APIs.
The authors introduce a contrastive filter to ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and effort in reviewing our work! Below, we provide responses to address your concerns, with *Lines xxx* and *Section x* referring to specific parts of our paper. We hope these clarifications effectively resolve your concerns.
**Concern 1: An Ablative Analysis**
... | null | null | null | null | null | null |
Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage | Accept (poster) | Summary: The present paper introduces an approach to weighted conformal prediction that employs quantile regression as a solution. The authors study general weights depending both on covariates and labels and provide theoretical guarantees via high probability upper bounds. Several particular examples of weighting func... | Rebuttal 1:
Rebuttal: We thank you for your review. Below, we address your questions:
**Q1**. Most conformal prediction algorithms share a common structure, involving quantile regression followed by constructing prediction sets via comparison with a quantile threshold. However, significant differences emerge in the pr... | Summary: The paper proposes Kandinsky Conformal Prediction method for general group-conditional guarantees in contrast to Mondrian conformal prediction that ensures conditional coverage over a disjoint set of groups. Their framework handles overlapping and fractional group memberships, and allows for group memberships ... | Rebuttal 1:
Rebuttal: We would like to thank you for your review. We address your question and your comment below.
### 1. I would like to see more details regarding the implementation of the algorithm (e.g., expanding section A.3), especially as the code was not shared in the current submission.
This links to the cod... | Summary: The paper proposes an extension of the conditional coverage works of Jung et al. and Gibbs et al. to functions of covariates and labels, not just covariates as in these previous works. Experiments on ACSIncome and CivilComments datasets are included.
Claims And Evidence: Claims are supported by the evidence i... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback. We address your concerns below.
### 1. “The paper provides the same flavor of bounds as GCC'24, but for weight functions that depend on both X and Y. Some initial work in this direction was completed by Blot et al (https://arxiv.org/abs/2406.17819), but t... | Summary: This paper is considering the problem of conformal prediction with conditional guarantees, particular allowing the conditioning event to be both a function of covariate X and label Y. They build upon the previously proposed technique in the literature which is based on training a linear quantile regression ove... | Rebuttal 1:
Rebuttal: We would like to thank you for your review. We answer your question below.
### 1. “... how should we design the basis (\phi) in practice? … how do we systematically figure out what kind of conditioning event we have to include or more generally what kind of distribution shifts we have to consider... | Summary: The authors build on the rich literature on extensions of the conformal prediction framework, that traditionally yields intervals where marginal probabilistic calibration is true.
More specifically, they propose a method able to infuse very generic notions of group-conditional calibration, moving from previous... | Rebuttal 1:
Rebuttal: We would like to thank you for your valuable comments. We address your questions and concerns below.
### 1. “It is unclear why the proposed method performs better…Kandinsky beats other methods.” “Your test examples seem to show… alternatives allround?”
We provide two concrete example scenarios w... | Summary: This paper proposes a new conditional conformal prediction framework named **Kandinsky conformal prediction** , which considers the conditional coverage given the information from both covariate and label. The theoretical results also improved existing bounds.
Claims And Evidence: Yes.
Methods And Evaluation... | Rebuttal 1:
Rebuttal: We would like to thank you for your feedback. We address your comments below.
### 1. “Add explicit definition of C(X_{n+1}) in Algorithm 3.”
The definition of $C(X_{n+1})$ is given in the equation of the second line of Algorithm 3, where it includes all $y$ such that the score function on the te... | null | null |
Boost-and-Skip: A Simple Guidance-Free Diffusion for Minority Generation | Accept (poster) | Summary: Authors of this work propose a method called "Boost-and-Skip" for generating minority samples from low-density regions in a data manifold using diffusion models. This method relies on two key modifications to the standard denoising process: 1) Initializing the reverse process with a higher variance noise (inst... | Rebuttal 1:
Rebuttal: We greatly appreciate Reviewer o8Xg for the strong acceptance and thoughtful feedback. Below, we provide detailed point-by-point responses to address your remaining concerns.
---
> **1. [o8Xg] questioned the effectiveness of our approach on highly biased datasets.**
To address your concern, we ... | Summary: The paper proposes Boost-and-Skip, a method to generate low-density, minority samples. The method has two straightforward yet effective modifications to standard diffusion models: (1) variance-boosted initialization, and (ii) timestep skipping during the generative process. The authors provide intuitions, theo... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful feedback and for considering our work for acceptance. We appreciate your time and evaluation. If you have any further suggestions or questions, feel free to let us know. We are more than happy to address any additional points or provide further clarifications as neede... | Summary: The paper proposes an approach called Boost-and-Skip for generating minority samples using diffusion models. Specifically, it begins stochastic generation with variance-boosted noise to encourage initializations in low-density regions. It then skips several of the earliest timesteps to further amplify the impa... | Rebuttal 1:
Rebuttal: We appreciate the reviewer for your detailed comments and valuable suggestions. Below, we provide thorough point-by-point responses to address your concerns.
---
> **1. [7UiG] expressed a concern on the sensitivity to hyperparameters.**
Please refer to our response to Reviewer o8Xg (the second ... | Summary: The paper provides two techniques for improving minority sampling. The first technique is a boost, which initialises the sampling with controllable variance. The second technique is skip where it will skip several sampling timesteps. The authors claim to achieve better performance in generating minority sample... | Rebuttal 1:
Rebuttal: We thank Reviewer bs5F for the constructive feedback. Below we provide point-by-point responses on your questions and concerns.
---
> **1. [bs5F] expressed a concern that the performance is often limited compared to baselines.**
We note that the superior baselines in Table 1 (e.g., [1,2]) corres... | null | null | null | null | null | null |
Beyond Confidence: Exploiting Homogeneous Pattern for Semi-Supervised Semantic Segmentation | Accept (poster) | Summary: This article proposes a new metric, AgScore, for pseudo label filtering. It measures the accuracy of pseudo-labels by evaluating the similarity between a pixel's embedding and positive pixel embeddings, as well as the dissimilarity between it and negative pixel embeddings. This method can be integrated as a un... | Rebuttal 1:
Rebuttal: Thanks for taking the time to share your comments in the review assessment. We provide a detailed point-by-point response to your comments.
Note that the following **link** refers to https://anonymous.4open.science/r/AgScore/Rebuttal.pdf
---
**Q1**: Advantages of AgScore.
**A1**: Semi-supervis... | Summary: This paper focuses on the confidence-based scoring functions in the semi-supervised semantic segmentation task. An agent construction strategy, aka., AgScore, is proposed to build clean sets of correct and incorrect pseudo labels. Experiments on three datasets show performance improvements in semi-supervised s... | Rebuttal 1:
Rebuttal: Thanks for taking the time to share your comments in the review assessment, as well as for acknowledging the **new perspective**, **well-supported motivation** and **clear idea**. We provide a detailed point-by-point response to your comments.
Note that the following **link** refers to
https:/... | Summary: This study focuses on semi-supervised semantic segmentation which struggles to effectively use unlabeled data due to challenges in balancing true and false positives when filtering pseudo labels. This paper introduces an agent construction strategy and the Agent Score function (AgScore) to better identify corr... | Rebuttal 1:
Rebuttal: Thanks for taking the time to share your comments in the review assessment. We provide a detailed point-by-point response to your comments.
**link** refers to https://anonymous.4open.science/r/AgScore/Rebuttal.pdf
---
**Q1:** More Evidence.
**A1**:
- Claim 1: To demonstrate the generalization... | Summary: The authors introduce “AgScore” (Agent Score), a scoring function to filter out unreliable pseudo labels at the pixel level in order to improve the performance of existing semi-supervised semantic segmentation (SSSS) methods. Unlike prior work that primarily relies on high-confidence thresholding in the predic... | Rebuttal 1:
Rebuttal: Thanks for taking the time to share your comments in the review assessment, as well as for acknowledging the **well-supported core claim**, **simple but effective idea**, **systematic experimental validation**, and **insightful theoretical analysis**. We provide a detailed point-by-point response ... | null | null | null | null | null | null |
AffinityFlow: Guided Flows for Antibody Affinity Maturation | Accept (poster) | Summary: This manuscript proposes an alternating optimization framework for designing antibodies. In the first stage of the cycle, for a given fixed sequence, structures are generated with high binding affinity using a (structure-based) predictor guidance of AlphaFlow. In the second, the structures are inverse-folded t... | Rebuttal 1:
Rebuttal: ## General Reply
Thank you for your insightful comments—they’ve greatly improved our manuscript. We’ve addressed each point and will update the manuscript accordingly.
## Claims and Evidence
> poor mapping
Thank you for your feedback. We agree that the gap between in silico proxies and experim... | Summary: The authors propose a pipeline to optimize sequences with structural guidance. AffinityFlow builds on AlphaFlow, a sequence-conditioned generative model. They present a two-stage optimization process: first, structure generation using a fixed sequence to guide the structure toward high binding affinity, follow... | Rebuttal 1:
Rebuttal: ## General Reply
Thank you for your constructive feedback, which has improved the clarity and rigor of our paper. We have addressed all points and will revise the manuscript accordingly.
## Methods And Evaluation Criteria:
> The clarity of the methods (predictor training).
To further clarify o... | Summary: The work combines classifier (gradient) guidance with Alphaflow for flow matching based antibody structure optimization to enhance binding affinity and performs inverse folding with ProteinMPNN to retrieve antibody sequences for synthesis. It also proposes a noise reduction framework (co-teaching) for labeled ... | Rebuttal 1:
Rebuttal: ## General Reply
Your insightful comments have greatly contributed to improving our manuscript, and we sincerely appreciate your time and effort. Each point you mentioned has been addressed, and the manuscript will be updated to reflect these improvements.
## Methods And Evaluation Criteria:
> G... | Summary: The paper proposes the AffinityFlow model and constructs an optimization framework for generating high-affinity antibodies. First, it utilizes a structure-based affinity predictor to guide the generation of antibody structures. Subsequently, it creates sequence mutations through inverse folding. This model ena... | Rebuttal 1:
Rebuttal: ## General Reply
Thank you for your valuable feedback, which has greatly improved our manuscript. We have addressed each comment and will incorporate the revisions accordingly.
## Claims And Evidence
> I think the architecture of the model is not clearly described. There is relatively little di... | null | null | null | null | null | null |
Tensor Product Attention Is All You Need | Reject | Summary: The authors propose a straightforward drop-in replacement for multi-head attention that they call Tensor Product Attention (TPA). The core idea is to compute queries, keys, and values using tensor products. The authors show that TPA can substantially reduce KV cache memory footprint, and that TPA can handle Ro... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful review and constructive feedback on our submission. Your comments have provided valuable insights that will help us improve the clarity and impact of our work. We appreciate your positive assessment and recommendation. Below, we address each of your points in detail a... | Summary: The paper proposes a new parameterization for the QKV activations that arguably is even simpler than the multi-head latent attention from deepseek.
The paper calls its method “tensor product attention” and connects to higher order tensor products in Appendix B but if I understood the paper correctly, all of ... | Rebuttal 1:
Rebuttal: We sincerely appreciate your insightful feedback and constructive comments. We have carefully considered all concerns and we provide detailed responses to your questions below.
> Q1: The paper describes a few different variants of their basic idea, such as making part of the QKV computation non-c... | Summary: This paper proposes Tensor Product Attention, which uses contextual tensor-decompositions. Based on TPA, the authors propose a new model architecture T6 for sequence modeling and adapt it with LLama and Gemma.
Claims And Evidence: N/A
Methods And Evaluation Criteria: The evaluation criteria make sense.
Theo... | Rebuttal 1:
Rebuttal: Thank you for your thorough review and constructive feedback on our submission. We appreciate your recognition of the novelty and clarity, and your detailed questions help us refine our manuscript.
> Q1: Despite reducing the number of parameters per token, TPA does not decrease the GPU memory usa... | Summary: The paper describes Tensor Product Attention (TPA), a type of attention mechanism, where queries, keys, and values are represented in low-rank factorized format. The authors claim the proposed method yields to the cache memory reduction during inference while preserving model quality. A neural network arch... | null | null | null | null | null | null | |
DiMa: Understanding the Hardness of Online Matching Problems via Diffusion Models | Accept (poster) | Summary: The authors study the hardness of the online bipartite matching (OBM) problem using denoising diffusion probabilistic models (DDPMs). The DDPMs are trained using policy gradient to generate hard instances for OBM. For classic OBM problem this represents the hardest input showing the validity of this approach.... | Rebuttal 1:
Rebuttal: We appreciate your constructive review and support of our work in applying AI techniques to theoretical computer science. This encourages us to study further in this direction. We hope the following responses will address your concerns and look forward to your ongoing support:
W1: While apparentl... | Summary: The paper presents a method based on a diffusion model trained using reinforcement learning. This model is then used to generate difficult instances for specific algorithms in online bipartite matching problems. The method successfully generates hard examples in two variants of the online bipartite matching pr... | Rebuttal 1:
Rebuttal: We sincerely appreciate your constructive feedback and acknowledgment that "it is a valuable contribution to the development of applied methods that can improve theoretical analysis." We hope the following responses address your concerns and look forward to your ongoing support:
Comments or Sugge... | Summary: The paper introduces a novel framework called DiMa, which enhances the theoretical understanding of Online Bipartite Matching (OBM) problems using diffusion models. DiMa models the generation of hard instances as a denoising process and optimizes them using a new reinforcement learning algorithm called Shortcu... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and insightful reviews and for positively evaluating our paper and contributions as “promising, compelling, and important”. We hope the following clarifications will address your concerns and look forward to your reconsideration of our work. Our supplemental result... | Summary: In this paper, they train a diffusion model to construct hard instances for Online Bipartite Matching problem.
Using the proposed method they find state-of-the-art upper bounds for the random arrivals and stochastic arrivals variants of Online Bipartite Matching problem.
For the training of the diffusion mode... | Rebuttal 1:
Rebuttal: We sincerely appreciate your thorough review and positive review of our work as "clear and convincing". We hope the followings address your concerns and look forward to your kind reconsideration of our work:
Generalizability(also Q2): We believe our DiMa demonstrates strong generalizability. We e... | null | null | null | null | null | null |
Quantifying Treatment Effects: Estimating Risk Ratios via Observational Studies | Accept (poster) | Summary: This paper develops novel estimators for the Risk Ratio (RR) in observational studies to accommodate confounding in non-randomized settings. It introduces estimators based on inverse propensity weighting, the G-formula, and doubly robust techniques, and establishes their asymptotic normality. Simulation studie... | Rebuttal 1:
Rebuttal: - **Figure Caption Clarity:**
Thank you for pointing this out. We agree that the figure captions could be clearer. In particular, we will revise the caption to explicitly mention that the colors represent the "Sample Size".
- **Doubly Robust Conditions:**
You are absolutely right that in ... | Summary: The authors propose several estimators for the average risk ratio (RR). They begin by analyzing the RR version of the Neyman estimator under standard causal inference assumptions, proving its asymptotic normality and deriving an expression for its variance. They then extend this analysis to an RR variant of th... | Rebuttal 1:
Rebuttal: - **Clarifying the Novelty and Contribution:**
We thank you for acknowledging the thoroughness of both the theoretical development and empirical validation. We would like to take this opportunity to clarify the main contribution of the paper.
One of the main contributions of this paper is t... | Summary: The authors discussed theory of risk ratio estimation in observational data. Several RR estimators are proposed with theoretical investigation, including asymptotic normality and confidence intervals. Two doubly robust estimators are proposed and the authors recommended the use of one of them.
Claims And Evid... | Rebuttal 1:
Rebuttal: Thank you for your thoughtful and constructive review. We appreciate your positive comments on the clarity of the writing and the contribution of our work to the causal inference literature. Please find our responses to your comments below:
1. **Regarding the assumption $E[Y(0) \mid X] > 0$:**
... | Summary: Authors focus on risk ratio (RR), a measure of treatment effectiveness complementary to the more common "risk difference". They first analyze the standard estimators for RR in an RCT and derive some new asymptotic normality/variance results, such as for continuous outcomes in addition to binary outcomes. They ... | Rebuttal 1:
Rebuttal: Thank you very much for your positive review. We are glad that you found the paper well-written, well-structured with interesting results for practitioners. We also appreciate your suggestion regarding the use of histograms to illustrate the asymptotic normality of our estimators. We agree that th... | null | null | null | null | null | null |
FairPFN: A Tabular Foundation Model for Causal Fairness | Accept (poster) | Summary: FairPFN is a tabular foundation model designed to address algorithmic bias in machine learning without requiring prior knowledge of the underlying causal model. Existing causal fairness methods rely on predefined causal structures, limiting their applicability in complex real-world scenarios. FairPFN overcomes... | Rebuttal 1:
Rebuttal: We would like to thank you for your detailed response! We have outlined our clarifications and proposed changes below:
> When the author tries to extend counterfactual fairness to the dataset level, why is the sensitive attribute changed under X rather than Y^hat, as described in Theorem 3.1?
W... | Summary: Let $A$ denote (binary) protected attributes, $X$ features, and $Y$ a binary response variable. A FairPFN is a transformer trained on synthetic data in such a way that when conditioned on $(A, X_{bias}, Y_{bias})$ it is encouraged to complete a query from the same distribution, denoted $X_{bias}^{val}$ with n... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments! Detailed responses below. Most importantly, we would like to clarify our perspective on the mathematical foundation of our method and how it relates to PPDs.
> In Line 238, “FairPFN thus approximates a modified PPD,...,” pointing to the PPD in Equation 3. T... | Summary: The paper introduces FairPFN, a tabular foundation model for causal fairness in machine learning. Pre-trained on synthetic causal fairness data, it mitigates the influence of protected attributes without requiring prior causal knowledge. Experiments show FairPFN effectively removes causal bias while maintainin... | Rebuttal 1:
Rebuttal: We thank you for your constructive feedback! In response, we have: (1) clarified that FairPFN requires no retraining for new applications (2) added analysis on intersectional fairness through a synthetic case study; (3) clarified the availability of inference and pre-training data generation code;... | null | null | null | null | null | null | null | null |
Adaptive Elicitation of Latent Information Using Natural Language | Accept (poster) | Summary: This paper studies the problem of adaptively selecting queries to reduce uncertainty on a latent entity. They propose to fine-tune an LLM for meta-learning the task of question answering with a latent entity (e.g. the 20 questions game with different hidden entities). This approach allows for measuring uncerta... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback. We are encouraged that the reviewer found the problem setting to be well motivated, and our experiments and results to be presented clearly. Please see our responses to the particular concerns raised.
**[Q1: Calculating confidence]**
Confid... | Summary: Authors consider a meta-learning QA scenario in which each dataset contains an unobservable latent variable (e.g., medical notes with an unseen clinical diagnosis). They propose an iterative and adaptive framework designed to reduce uncertainty around these latent variables.
Claims And Evidence: **Claim 1.** ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful and thorough review, and their recognition that our claims are well-supported by experiments. We response below to the concerns.
**[Q1: Use of "counterfactual response"]**
We apologize for the confusing use of the term "counterfactual response" in the ... | Summary: This paper introduces an adaptive elicitation framework for actively reducing uncertainty about latent entities, using adaptive query selection and simulated counterfactual responses. It leverages a meta-trained LLM to quantify uncertainty about future or unobserved answers via simulation, then iteratively sel... | Rebuttal 1:
Rebuttal: We thank the reviewer for their effort in reviewing our paper, and their thoughtful feedback. We are pleased that the reviewer appreciated the importance of the topic as well as the novelty of our approach. Below please see our responses to the particular concerns raised.
**[Q1: Sensitivity to ... | Summary: This paper introduces a framework for adaptive elicitation of latent information using LLMs to optimize question selection based on predictive uncertainty. Instead of explicitly modeling latent variables (e.g., knowledge levels or user preferences), the method quantifies uncertainty via LLM perplexity and simu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the time and consideration taken in reviewing our paper, and for recognizing our contribution to the application of uncertainty-driven question selection in important domains like education and healthcare. Below, we respond to your particular concerns.
**[Q1: Modeling t... | null | null | null | null | null | null |
Contrastive Learning with Simplicial Convolutional Networks for Short-Text Classification | Accept (poster) | Summary: This work proposes C-SCN, which combines contrastive learning with simplicial complexes in convolutional networks to capture higher-order interactions and improve short-text classification performance. Experimental results demonstrate its superiority over existing methods.
Claims And Evidence: The work identi... | Rebuttal 1:
Rebuttal: **(W1) The work identifies three issues with current methods. However, it remains unclear how the proposed methods specifically address these issues.**
Due to the word limit constraints, we refer to the response to Reviewer jGxX (W3) for similar concerns.
**(W2) SCN is used to capture higher-or... | Summary: The paper proposes Contrastive Learning with Simplicial Convolutional Networks (C-SCN) for short-text classification. The method constructs document simplicial complexes to capture higher-order interactions beyond simple pairwise relationships and integrates a contrastive learning framework that leverages both... | Rebuttal 1:
Rebuttal: **(W1) The paper does not adequately justify the necessity of a complex graph-based model when simple large language models (LLMs) could potentially address these issues.**
We would like to highlight the novelty of our work in pioneering the use of higher-order simplicial complexes for short text... | Summary: Due to limited labels and sparsity in words and semantics, short text caught much attention. Most of the current models adopted self-supervised contrastive learning across different representations but generate samples and external auxiliary information can not guarantee the effectiveness. And they also can ... | Rebuttal 1:
Rebuttal: **(W1) This paper has compared the proposed model with several baselines. They provide definitions and methodology. However, more theoretical proof is demanded.**
We would like to refer to the following sources for theoretical proof. To compare the expressiveness of graph neural networks and neur... | null | null | null | null | null | null | null | null |
The Number of Trials Matters in Infinite-Horizon General-Utility Markov Decision Processes | Accept (spotlight poster) | Summary: This paper investigates how the number of trials affects policy evaluation in infinite-horizon general-utility Markov Decision Processes (GUMDPs) for both the discounted and average cases. For the discounted case, the authors demonstrate that a mismatch generally exists between the finite- and infinite-trial f... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review, comments, and concerns raised. We answer below the questions/concerns raised:
- *"Line 377 (left): "Both $K$ and $H$ contribute to the tightness of the upper bound." However, under Theorem 4.3, the authors mention that "Finally, the upper bound does n... | Summary: The paper analyzes the impact of the number of trails in estimating the objectives for GUMDPs. For both the discounted and average settings, it is shown by examples that there are mismatches between the finite-trial estimates and the actual infinite-trail objectives. Bounds on the mismatches are provided, with... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review, comments, and concerns raised. We answer below the questions/concerns raised:
- *"In Fig 3b, it looks like there are some discontinuities in the performance of around where the finite-trail performance seems to diverge away from the infinite-trail one... | Summary: This paper analyzes the impact of the number of sampled trajectories on the estimation of the return for infinite-horizon MDPs with a general utility function. The classical MDP setup corresponds to linear utility, and in this case there is no bias induced by considering only a finite number of sampled traject... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive review, comments, and concerns raised. We answer below the questions/concerns raised:
- *"Minus sign in the definition of the expected discounted cumulative reward*": We focused on the case of minimization (hence, convex $f$), similarly to what has been done ... | Summary: The paper continues work in the area of the so-called general utility MDPs. The main contribution is the clarification that infinite horizon criteria will not close the "finite vs infinite trial gap" contrary to a suggestion by Mutti et al. (2023) who studied the gap in the finite horizon setting.
Specificall... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and concerns raised. We answer below the questions/concerns raised:
- *"Is there a way to justify looking at this problem given that the infinite horizon setting is an idealization and the whole motivation is to move closer to realism?"*: We agree and acknowl... | Summary: This paper focus on General-utility MDP (GUMDPs) generalizes the MDPs framework by considering convex objective functions $f$ of the occupancy frequency $d$ induced by a given policy. The authors analyze the discrepancy between the finite-trials formulation $f_K$ and the infinite-trials formulation $f_\infty$... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and concerns raised. We answer below each of the questions/concerns raised:
- *"(1) Which aspects are identical to [1] and which parts of the extension present significant challenges.":* We thank the reviewer for asking about the differences between our artic... | Summary: This paper analyzes infinite-horizon GUMDPs, highlighting the critical role of the number of sampled trajectories in policy evaluation. The authors theoretically and empirically demonstrate that, unlike standard MDPs, GUMDP performance metrics significantly depend on the number of trials, presenting theoretica... | Rebuttal 1:
Rebuttal: We thank the reviewer for the comments and concerns raised. We answer below the questions/concerns raised:
- *"Have the authors considered how their theoretical results hold when relaxing assumptions on utility function convexity or continuity, which might often be violated in real-world scenario... | null | null |
Flowing Datasets with Wasserstein over Wasserstein Gradient Flows | Accept (oral) | Summary: This work presents a novel theoretical framework for handling probability distributions over probability distributions. The authors begin by rigorously establishing the Wasserstein over Wasserstein (WoW) distance metric from a functional analysis perspective, providing a mathematical foundation for measuring d... | Rebuttal 1:
Rebuttal: Thank you for your appraisal and positive comments on our paper. We address your comments below.
**Domain Specification of the Functional**
For most of ML applications where we aim at minimizing distances, we agree that the codomain is $\mathbb{R}_+\cup \\{+\infty\\}$. Nonetheless, the different... | Summary: The paper proposes a framework for optimizing functions over probability measure spaces of probability measures. The approach is based on Wasserstein over Wasserstein gradient flows. The main contribution is a theoretical definition of this flow. The author also introduces objectives that are tractable within... | Rebuttal 1:
Rebuttal: Thank you for your appraisal and positive comments on our paper. We answer your comments below.
**The qualitative results (Figures) are not entirely convincing and do not clearly suggest a continuous flow from one dataset to another.**
We observed empirically that the flow goes very fast from th... | Summary: This paper introduces a framework for optimizing functionals over probability measures of probability measures by leveraging the Riemannian structure of this space to develop Wasserstein over Wasserstein (WoW) gradient flows. It provides a theoretical foundation for these flows and a practical implementation u... | Rebuttal 1:
Rebuttal: Thank you for reading the paper and for your feedback. We answer your comments below. Please do not hesitate if you have other questions.
**The continuity equation relies on the strong assumption that the velocity field vt is Lipschitz.**
In Proposition 3.7, we show that if $(\mathbb{P}\_t)\_t$... | null | null | null | null | null | null | null | null |
Why Has Predicting Downstream Capabilities of Frontier AI Models with Scale Remained Elusive? | Accept (poster) | Summary: The paper investigated why we can not accurately transform the scaling laws from the general negative token likelihood to the downstream accuracy of multi-choice QA.
The main reasons are accountable for the phenomenon are 1. there is a sequence of transformation from NLL to accuracy; 2. the positive correlati... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer Zmym for their thoughtful assessment of our work, particularly noting that our paper is "pretty clear" and addresses an "important" question with "good experimental settings."
### Quantifying Score-Compute Decorrelation Per Transformation
> Since we know that the tras... | Summary: This paper explores why predicting the downstream performance of advanced AI systems, especially on multiple-choice question-answering benchmarks, is difficult despite well-understood scaling laws during pre-training. The key finding is that downstream performance degrades as it involves comparing the correct ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your thorough and thoughtful review. We greatly appreciate that you found our analysis "interesting," "quite comprehensive," and that it "gets at the main cause" of unpredictability in downstream capabilities. We're particularly pleased that you found our story "presente... | Summary: The paper studies the relationship between model scale and downstream task performance, to understand why it has been hard to formulate a "scaling law" to describe the relationship unlike known results for pretraining performance.
The paper conducts extensive empirical analyses on many tasks, benchmark dataset... | null | null | null | null | null | null | null | null | |
QuEST: Stable Training of LLMs with 1-Bit Weights and Activations | Accept (poster) | Summary: The paper presents QuEST, a quantization-aware training (QAT) method that enables training LLMs with extremely low-precision weights and activations (both down to 1-bit). The key contributions are: (1) Hadamard normalization with MSE-optimal fitting for quantization, and (2) a trust gradient estimator that min... | Rebuttal 1:
Rebuttal: > Why is the method named QuEST?
We apologize for the omission, QuEST stands for **Qu**antized, **E**fficient, and **S**table **T**raining.
> Can you explain the overlapping W3A3 and W4A4 curves in Figure 1?
The decrease in training precision corresponds to fixed parameter count models shiftin... | Summary: In this paper, the authors propose QuEST, a low-bit quantization-aware training (QAT) method aimed at compressing models more accurately. Experiments demonstrate that QuEST outperforms the LSQ method in performance under various low-bit weight and activation quantization scenarios. Additionally, based on the d... | Rebuttal 1:
Rebuttal: > Detailed discussion on how its (QuEST’s) advantages manifest.
The fact that QuEST enables stable training across scales has the following implications:
1. If using QuEST, INT4 is the “optimal” bit-width for weights and activations in terms of inference effectiveness, that is, the accuracy that... | Summary: This paper explores how to improve quantization aware training. Following recent work in post-training quantization, this paper proposes a combination of techniques it calls QuEST. QuEST involves using a Hadamard Transform in the forward pass to improve the quantization process and introduces "trust estimati... | Rebuttal 1:
Rebuttal: Thank you for the detailed review! We address all your questions below.
> I'm not convinced that the 'trust factor' approach to masking out gradients is well motivated.
Thank you for raising this. First, trust factor masking is motivated theoretically by directly targeting the source of error ... | null | null | null | null | null | null | null | null |
Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning | Accept (spotlight poster) | Summary: The paper introduces a Log-Sum-Exponential (LSE) estimator to address off-policy evaluation (OPE) and learning (OPL) in contextual bandit settings where rewards or propensity scores may be noisy or heavy-tailed. By applying a log-sum-exp transformation over importance-weighted rewards, this estimator improves ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their careful reading and comments on the paper. We will address their concerns as detailed below.
> single-pass aggregator
**R1:** Thanks for raising this point. It is true that theoretically, LSE is not separable and should be applied to the whole dataset. But, due t... | Summary: The paper introduces a novel Log-Sum-Exponential (LSE) estimator for off-policy evaluation (OPE) and off-policy learning (OPL) in reinforcement learning, especially when dealing with logged bandit feedback datasets that may contain unbounded or heavy-tailed rewards. The paper analyzes the LSE estimator's regre... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
> The simulation experiments are comprehensive and convincing. That said, I think the experiments and validation could be stronger if data from RCTs can be ... | Summary: The paper proposes an estimator based on the log-sum-exponential (LSE) operator designed for off-policy evaluation (OPE) and off-policy learning (OPL) in contextual bandit settings. The LSE estimator addresses the issue of high variance in inverse propensity score (IPS) estimators by introducing robustness to ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
> Novelty
**R1:** We appreciate the reviewer’s feedback and the opportunity to clarify the novelty of our work. While it is true that we employ the log-sum... | Summary: The paper proposes to use the log-sum-exponential operation as an off-policy estimator, proving bounds on the mean and variance of the estimates, as well as the performance gap between the optimal and learned policies in off-policy learning, and convergence rates. They follow this up with empirical evaluations... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments, and generally positive assessment of the paper. We will address their concerns as detailed below.
> It seems that these results could potentially be related to a Bayesian setting with exponential family distributions. Have the authors thought about this... | null | null | null | null | null | null |
CoMemo: LVLMs Need Image Context with Image Memory | Accept (poster) | Summary: - This paper proposes CoMemo, a hybrid architecture of LLaVA and Flamingo’s cross-attention module.
- It also has a 2D positional encoding mechanism (RoPE-2D) to better preserve spatial relationships, addressing the issue of visual information neglect in long contexts.
- Experimental results across multiple be... | Rebuttal 1:
Rebuttal: `Q1: CoMemo is essentially a hybrid of LLaVA and cross-attention (Flamingo-style). `
First, neither open-source nor closed-source models currently offer such an architecture. As Reviewer vTuL noted, "It is exciting to see an effective solution that leverages the advantages of both approaches." Fu... | Summary: The paper introduces CoMemo to improve visual information retention in multimodal tasks. Specifically, CoMemo includes a memory path for image tokens that operates independently of the main text path. This helps prevent visual information loss during long-context reasoning. Then CoMemo uses RoPE-2D encoding th... | Rebuttal 1:
Rebuttal: `Q1: The dual-path architecture and RoPE-2D introduce additional computational overhead, potentially impacting real-time applications.`
While CoMemo introduces some computational overhead, we have designed mechanisms to minimize latency, making the time difference between CoMemo and LVLM-S nearly... | Summary: This paper thoroughly investigates the flaws of LLM architectures when processing multimodal inputs, including the progressive neglect of central visual content as context expands and the failure of conventional positional encoding schemes in preserving 2D structures. To address these issues, this paper presen... | Rebuttal 1:
Rebuttal: Thank you very much for your recognition of our work and your positive comments regarding the well-orgnized and insightful findings of our paper.
Below, we will address your concerns point by point, and all suggested revisions will be incorporated into the next version of the manuscript. If our r... | Summary: This paper introduces CoMemo, provides two key design choices into MLLMs, 1) adding additional cross-attn like Flamingo, besides the origianl llava-style approach, but to mask part of the info to prevent over-reliance 2) add 2d rope to LLM backbone for image features. The approach ourperforms llava-style and f... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback to improve our work's clarity. As you rightly pointed out, proposing a framework that combines Flamingo-style and LLaVA-style approaches represents a highly impactful contribution. Thank you for recognizing the value of our work.
Below, we address your concerns... | null | null | null | null | null | null |
Automatic Reward Shaping from Confounded Offline Data | Accept (poster) | Summary: The paper aims to automate reward shaping when learning policies online via Reinforcement Learning (RL). Authors propose an automated approach for designing reward functions utilizing previously collected offline samples with unobserved confounders. State value upper bounds are calculated and used as a conserv... | Rebuttal 1:
Rebuttal: We thank the reviewer for the detailed feedback and appreciate your recognition of the significance of our work. We have addressed each of your concerns in the responses below.
> Weakness 1: “Could the authors provide if they inject any noise in the data distribution? How much confounding does th... | Summary: This paper addresses the challenge of designing reward shaping functions in reinforcement learning, particularly in the context of Potential-Based Reward Shaping, which traditionally relies on manually crafted potential functions. To overcome this limitation, the authors propose a data-driven approach that aut... | Rebuttal 1:
Rebuttal: We are grateful for your detailed comments. We have addressed your concerns in the sequel.
> Optimality of the learned policy & Question 1
While we adopt the regret analysis framework, we acknowledge that it offers only a weaker guarantee of convergence to the optimal policy. A PAC framework may... | Summary: This paper focuses on the automated design of a reward shaping function from offline data, potentially contaminated by unobserved confounding bias. The authors propose leveraging causal state value upper bounds from offline datasets as a conservative, optimistic estimate of the optimal state value, which is th... | Rebuttal 1:
Rebuttal: We thank you for your thoughtful feedback. We appreciate your acknowledgment of the significance of our work and have addressed your concerns in the sequel.
> “The only one concern is how would the proposed method in more realistic environments, like MuJoCo?”
In this paper, we primarily focus on... | null | null | null | null | null | null | null | null |
Universal Biological Sequence Reranking for Improved De Novo Peptide Sequencing | Accept (poster) | Summary: This paper presents RankNovo, a deep reranking framework for de novo peptide sequencing that integrates multiple sequencing models using a listwise reranking approach and axial attention to extract informative features. The introduction of PMD (Peptide Mass Deviation) and RMD (Residual Mass Deviation) provides... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments. We address your concerns as follows:
> A significance analysis is needed to strengthen the reliability of performance improvement.
Thank you for your suggestion. We agree that statistical significance analysis is essential for validating the reported improve... | Summary: The paper outlines a new reranking strategy, wherein the proposed meta-model is capable of ranking candidate peptide sequences for a given tandem spectrum. The proposed meta-model, RankNovo, innovates on existing approaches by (a) using axial attention to derive latent space representations, and (b) utilizing ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments. We address your concerns as follows:
> Is the choice of base models making benchmark comparison favorably for RankNovo? Should performance improvement be attributed to base models' capabilities or the reranking model?
Thank you for this important question ab... | Summary: This paper presents RankNovo, the deep reranking framework that enhances de novo peptide sequencing by leveraging the complementary strengths of multiple sequencing models.
Claims And Evidence: The claims made in the submission are supported by clear and convincing evidence.
Methods And Evaluation Criteria: ... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments. We address your concerns as follows:
> It's better to provide benchmark performance of RankNovo on NovoBench.
Thank you for your suggestion. Following your recommendation, we expanded our evaluation to include all three **NovoBench** data sources: **Seven-Sp... | Summary: This paper introduces RankNovo, an innovative deep reranking framework designed to enhance de novo peptide sequencing accuracy. RankNovo leverages the complementary strengths of multiple sequencing models to overcome the limitations of individual approaches.
RankNovo represents the first deep learning-based r... | Rebuttal 1:
Rebuttal: Thank you for your detailed comments. We address your concerns as follows:
> The paper lacks validation on more challenging datasets with diverse post-translational modifications (PTMs).
We sincerely appreciate your insightful observation regarding the need for validating RankNovo on datasets wi... | null | null | null | null | null | null |
Towards a Mechanistic Explanation of Diffusion Model Generalization | Accept (spotlight poster) | Summary: In this paper, the authors analyze inductive biases of diffusion and relate these to the generalization capabilities of diffusion models. The authors start by examining the network denoiser approximation error, where the approximation error is defined as the deviation of the prediction from the optimal denoise... | Rebuttal 1:
Rebuttal: ## General Comment
We'd like to thank the reviewer for their thorough review. We have summarized and responded to the key points of your review below in the limited available space. If we have missed any of your points, we welcome further discussion.
## Methods & Evaluation
**Network errors for $... | Summary: The paper studies the inductive bias of diffusion models that enable generalization. The authors attribute such inductive bias to the locally denoising capability of diffusion models, which is supported by the observation that the network outputs are sensitive to the local perturbation of its input.
Based on... | Rebuttal 1:
Rebuttal: ### **General Response**
We'd like to thank the reviewer for taking the time to read and review or work. Please find our responses to your review below. If there are any items which you believe have not been addressed, we welcome this feedback.
### **Other Comments Or Suggestions:**
> Are the la... | Summary: This paper attempts to explain the mechanism behind the ability of diffusion models to generalize beyond the training data. It starts by pointing out that this ability is due to the neural denoiser deviating from the optimal empirical denoiser (the optimal denoiser for the training set). It then shows that the... | Rebuttal 1:
Rebuttal: ### **General Comment**
We'd like to thank the reviewer for taking the time to read and review our paper. Furthermore, we appreciate the additional references that the reviewer has brought to our attention. Below, we have responded to several items of your review. If there are items which you fee... | Summary: That real diffusion models generalize their training data, rather than memorize it, is not obvious: the optimal solution to a typical denoising score matching objective is the score of the (empirical) data distribution, which can only reproduce training examples. Why do diffusion models generalize, and what in... | Rebuttal 1:
Rebuttal: ### **General Comment**
We'd like to thank the reviewer for their thoughtful consideration of our work. Below, we have responded to specific components of your review. If you have additional questions, we would welcome further discussion.
### **Relation To Broader Scientific Literature:**
>This p... | null | null | null | null | null | null |
Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging | Accept (poster) | Summary: Generalization capability is critical for FL in real-world applications. This paper revisits the generalization problem in FL, focusing on the impact of data heterogeneity. The authors propose FedSWA, which uses Stochastic Weight Averaging to find flatter minima, and a varaint FedMoSWA, a momentum-based varian... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will make modifications in the final version as suggested by the reviewer, and our point by point responses to your major comments are given below.
To address your concern, we conducted many experiments on CIFAR-100 with ResNet-18 under different data hetero... | Summary: Tackles generalization issues in FL with highly heterogeneous data.
Introduces a new momentum-based stochastic controlled weight averaging FL algorithm.
Claims And Evidence: Provides some theoretical guarantees and empirical results.
Methods And Evaluation Criteria: Evaluations are conducted on different da... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments. We will make modifications in the final version as suggested by the reviewer, and our point by point responses to your major comments are given below.
1. Different from FedSAM, the proposed FedSWA algorithm is a new way to improving FL generalization. By using ... | Summary: This paper proposes two novel algorithms for improving generalization in federated learning.
The first approach, FedSWA, is a variant of FedAvg with stochastic weight averaging, a method known for finding flatter minimums.
The second approach, FedMoSWA, extends FedSWA with control variates that are updates usi... | Rebuttal 1:
Rebuttal: We sincerely thank the Reviewer for their valuable comments, and our point by point responses to your major comments are given below.
1. For Experimental Designs Or Analyses. In fact, we have done the hyperparameter tuning for all the algorithms, and we also followed the parameter settings from ... | null | null | null | null | null | null | null | null |
LEAPS: A discrete neural sampler via locally equivariant networks | Accept (poster) | Summary: This paper introduces a continuous-time diffusion sampler that operates along an annealed energy path in the discrete domain. The approach is trained using a PINN-based objective as an upper bound for the Log Variance Loss. The authors propose locally invariant neural network architectures for parametrization.... | Rebuttal 1:
Rebuttal: Thank you for your thorough and valuable feedback on our manuscript. Below we itemize and address your comments and concerns.
**Further experiments and benchmarks:**
- We have included additional experiments, as possible within the limited time frame. Specifically, we run additional tests of the... | Summary: The goal of this paper is to draw samples from a distribution $\rho_1$, known up to a normalization constant, over a discrete space.
One way to do so is to simulate a prescribed path of marginal distributions ${(\rho_t)}_{t \in [0, 1]}$ that ends with the desired target.
To do so, the authors introduce a ge... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback on our work. Below, we provide answers to your questions and concerns.
**Additional benchmarks and experiments.** We obtained new experimental results. The results can be found under this anonymous link, in which we show that our method performs w... | Summary: The paper proposes locally equivariant functions, a compact neural parameterization of rate matrices in continuous-time Markov processes over discrete state spaces. This effectively allows them to use recent "discrete diffusion" models as proposals in annealed importance sampling and sequential Monte Carlo ov... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback on our work. Below we address your questions and provide new information on additional experiments.
**Updates to experiments: harder sampling problem and comparison benchmarks**
We obtained new experimental results and benchmarks as possible within the limite... | Summary: The authors propose an algorithm for sampling from discrete distributions by combining importance sampling with a learned continuous-time Markov chain (CTMC). They derive the importance weights via a Radon–Nikodym derivative for CTMCs and introduce locally equivariant neural architectures to ensure tractable l... | Rebuttal 1:
Rebuttal: We thank the reviewer for their valuable feedback on our work. We are glad to read that the reviewer considers our work a "sound" and "novel" contribution. Below, we provide answers to your questions and concerns.
We understand that the reviewer sees our experiments as the main area of improvemen... | null | null | null | null | null | null |
Reducing Variance of Stochastic Optimization for Approximating Nash Equilibria in Normal-Form Games | Accept (spotlight poster) | Summary: This paper proposes NAL, a loss function that is unbiased and has lower variance compared with the only unbiased loss function proposed in Gemp et al. (2024). The paper conducts theoretical and empirical justifications to show that NAL theoretically and empirically exhibits lower variance and thus accelerates ... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment and helpful suggestions.
**W1: The contribution of lower variance builds upon the idea of Gemp et al. (2024), which may slightly decrease the paper's originality.**
**A:** Both our work and Gemp et al. (2024) explore leveraging the stochastic optimization t... | Summary: This paper studies computing Nash equilibria (NE) in normal-form games using non-convex stochastic optimization techniques from machine learning. The existing unbiased loss function for approximating NE has high variance, which degrades the convergence rate. To address this, the authors propose a novel surroga... | Rebuttal 1:
Rebuttal: We sincerely appreciate your time and thoughtful review of our manuscript. Your positive feedback are truly encouraging our work. This paper focuses on leveraging the stochastic optimization techniques in ML to approximate NE of normal-form games. We propose a novel surrogate loss function, which ... | Summary: The authors tackle the problem of NE computation in general-sum multiplayer NFGs, which is known to be a computational hard problem. They build on the work of Gemp et al. (2024) to come up with a novel approach involving a surrogate loss function they call Nash Advantage Loss. They show that NAL is unbiased an... | Rebuttal 1:
Rebuttal: Thanks for your valuable and insightful comments.
**Q1: The use of undefined symbols in the Related Work section.**
**A:** We fully agree with your observation. The use of undefined symbols compromises the internal consistency of the paper. We will revise our paper to ensure that all symbols a... | Summary: This paper addresses the challenge of efficiently computing Nash equilibria (NE) in normal-form games (NFGs) via non-convex optimization. Prior work, notably by Gemp et al. (2024), proposed an unbiased loss function for this purpose but encountered high variance, which hindered convergence. To overcome this, t... | Rebuttal 1:
Rebuttal: Thank you for your recognition of our work. In response to your suggestion regarding readability, we have added a table of notations and definitions, as presented in Table 1 of https://anonymous.4open.science/api/repo/ICML-2025-ID-10862-Rebuttal/file/notation-table.pdf. | null | null | null | null | null | null |
FSL-SAGE: Accelerating Federated Split Learning via Smashed Activation Gradient Estimation | Accept (poster) | Summary: The paper introduces FSL-SAGE, a framework designed to address the limitations of Federated Learning (FL) and Split Learning (SL). FL struggles with training large models due to client-side memory constraints, while SL incurs high communication latency due to sequential processing. FSL-SAGE combines the data p... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Please refer to the abbreviations in reviewer **HNw2**'s rebuttal.
> **Comment 1:** Experimental validation relies on small-scale models. Weakens claim that FSL-SAGE applies to large models.
**Response:** We appreciate the reviewer's point about using l... | Summary: The paper studies split federated learning with a focus on reducing training latency/communication. It uses an auxiliary model to facilitate the computation of cut-layer gradients. To mitigate potential accuracy drop, the paper aligns the auxiliary model with the server-side model periodically. A solid converg... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. Please refer to the abbreviations in reviewer **HNw2**'s rebuttal.
> **Comment 1:** In Theorem 4.3, physical meaning of $c$?
**Response:** Thank you, we missed defining the constant $c$ in **Theorem 4.3**, but it is defined later in **Theorem 4.8**. We ... | Summary: The paper addresses the computational burden faced by clients when performing local updates on whole (possibly large) models by splitting the model into server-side and client-side components, following the approach used in prior split learning (SL)-based methods. Unlike previous works, the authors propose an ... | Rebuttal 1:
Rebuttal: We thank the reviewer for their comments. For brevity we will use the abbreviations FSL for federated split learning, FL (SL) for Federated (resp. Split) Learning, CSM (SSM) for client-side (resp. server-side) model, and AM for auxiliary model.
> **Comment 1:** Sending alignment dataset to serve... | Summary: The paper proposed a new federated split learning algorithm called FSL-SAGE. It builds upon existing works on local split federated learning, where client updates are derived from local approximations using an auxiliary model attached to each client. A key challenge with previous approaches is that, due to the... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions. Due to space limitation, we could only respond to a subset of more critical comments in this rebuttal. But we are happy to continue to complete our responses to your remaining comments in the discussion stage when new space opens up. Please refer to th... | null | null | null | null | null | null |
What If We Recaption Billions of Web Images with LLaMA-3? | Accept (poster) | Summary: The authors finetune a better Llava-1.5 model using a more advanced Llama model. The authors then recaption the DataComp-1B dataset and show promising results in terms of ImageNet zero-shot accuracy and text-to-image retrieval benchmarks. The authors finally use the CLIP score to show that the recaptioned-capt... | Rebuttal 1:
Rebuttal: ### **Q1: No algorithmic contributions.**
**A1:** While we don’t introduce new recaptioning algorithms, we highlight two core contributions:
Recap-DataComp-1B, the first fully open, billion-scale image-text dataset with synthetic captions generated using LLaMA-3. Prior works [1,2] are proprietar... | Summary: This paper explores the impact of improving textual descriptions for large-scale web-crawled image-text datasets using LLaMA-3. The authors propose a recaptioning pipeline that fine-tunes a LLaMA-3-8B-powered LLaVA-1.5 model and applies it to ∼1.3 billion images from the DataComp-1B dataset. The resulting data... | Rebuttal 1:
Rebuttal: ### **Q1: Novel ideas, insights, and conclusions.**
**A1:** Thank you for raising concerns about novelty. Our primary focus is on developing **Recap-DataComp-1B**, which we believe to be **the first publicly available image-text dataset at the billion-scale generated by LLaMA-3**. Unlike previou... | Summary: This paper explores the impact of recaptioning web-crawled image-text pairs using LLaMA-3. The authors identify that web-crawled datasets (like DataComp-1B) suffer from image-text misalignment and low-quality textual descriptions. Their approach is straightforward: they fine-tune a LLaMA-3-8B powered LLaVA-1.5... | Rebuttal 1:
Rebuttal: ### **Q1: Analysis of this limitation, insights, and technology to migrate classification performance issues.**
**A1:** Following your valuable suggestion, we analyze the classification drop with synthetic captions and identify three likely causes: (1) CLIP's difficulty learning from long, rich... | Summary: The paper introduces Recap-Datacomp-1B a large scale image-text data, where the text is "synthetically generated" using large multimodal models. The authors observed that compared to original web-crawled data, models trained on this generated synthetic texts are performing better for multimodal retrieval and ... | Rebuttal 1:
Rebuttal: Thank you for recognizing that our dataset significantly enhances performance in both multimodal retrieval and text-to-image generation tasks. We appreciate your insights into how advanced MLLMs can reduce reliance on costly human annotations and enable greater scalability.
Our Recap data relies s... | null | null | null | null | null | null |
CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition | Accept (poster) | Summary: This paper introduces CEGA for model extraction attacks against GNNs with limited query budgets. The authors address practical constraints in real-world attack scenarios by designing a node querying strategy that incrementally refines node selection over multiple learning cycles. CEGA integrates three key cons... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We provide a point-to-point reply below.
**Response to Review on Claims**: Thank you for pointing this out. We would like to clarify that the *cost-effectiveness* we attribute to CEGA refers to two major aspects. First, CEGA shows its ability to **achieve hig... | Summary: The paper proposes a method for model extraction attack, in the setting where the number of prediction queries is extremely tight.
Node predictions are queries in different rounds and based on three criteria: representativeness, uncertainty, and diversity.
For the entropy-based approach, time and space compl... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We provide a point-to-point reply below to address the mentioned questions and concerns.
> **Reviewer**: The improvements are often marginal, and sometimes significant. I'm not sure if the contribution is sufficient for an ICML paper. Moreover, most improveme... | Summary: This paper explores the vulnerability of Graph Neural Networks (GNNs) to Model Extraction Attacks (MEAs), particularly in scenarios with limited query budgets and initialization nodes. The authors propose a novel node querying strategy that incrementally refines the selection of nodes over multiple learning cy... | Rebuttal 1:
Rebuttal: Thank you for your comprehensive and thoughtful feedback towards the CEGA work. We sincerely appreciate the time and expertise you dedicated to carefully reviewing our manuscript. We would like to provide some further insights to our paper regarding your comments to fully address the potential con... | null | null | null | null | null | null | null | null |
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence | Accept (poster) | Summary: This paper tackles measuring variable importance in conditional average treatment effect (CATE) functions. One of the few current approaches consists in applying the LOCO method to CATE estimation ; as the CPI method is an alternative to LOCO, authors propose applying CPI to CATE estimation. They prove the con... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer. The comments and questions are relevant and will help us improve the paper.
# Discussion of assumption 3.1
- Assumption 3.1, that a given covariate can be decomposed as a function of the other covariates plus an additive independent noise term is quite common... | Summary: The submission describe a variable importance method (PermuCATE) generalizing CPI (Chamma et al. 2023),
a theoretical analysis of PermuCATE is performed showing the behaviour of the estimator in finite sample.
Extensive experiments are implemented over a variety of datasets comparing PermuCATE against LOCO.
... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer. The comments and questions are relevant and will help us improve the paper.
# Previously published references
- We would like to highlight that important references for this work have been published. Specifically, Chamma et al. 2023 in NeurIPS and Verdinell... | Summary: The paper proposes a new explainability method for the conditional average treatment effect (CATE) estimators, namely, PermuCATE. PermuCATE measures global features importances and is based on a conditional permutation importance (CPI) methods. The authors demonstrated that PermuCATE aims to estimate the expec... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review. The comments and questions are relevant and will help us improve the paper.
# Dependence of PermuCATE on the estimate of $\tau$
- To clarify the dependence of PermuCATE on the estimation of $\tau$, we first provide some intuition: LOCO and PermuCATE... | Summary: The paper proposes a variable importance measure (VIM) to understand the variables driving the conditional average treatment effect (CATE) function. The measure is based on the principle of conditional permutation importance where the variable of interest is permuted while keeping matching its conditional dist... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thorough review. The comments and questions are relevant and will help us improve the paper.
# Dependence of PermuCATE on the estimate of $\tau$
- We refer the reviewer to the response provided to Reviewer wUZa, in the section "Dependence of PermuCATE on the estimate... | null | null | null | null | null | null |
WMarkGPT: Watermarked Image Understanding via Multimodal Large Language Models | Accept (poster) | Summary: The paper introduces WMarkGPT, a multimodal large language model (MLLM) designed to understand watermarked images without requiring access to the original images. Specifically, it integrates a visual encoder, learnable queries, a visual abstractor, and an LLM to generate detailed descriptions of watermarks and... | Rebuttal 1:
Rebuttal: Thanks for the valuable comments and recognition of the novelty and meaningfulness of our research. The reviewer acknowledged the clarity and thoroughness of our experimental design, as well as the strong evidence backing our claims. They highlighted WMarkGPT’s superior performance in watermark de... | Summary: The authors innovatively propose a new multi-modal large language model WMarkGPT for watermarked image understanding. This paper points out that traditional methods rely on indicators such as PSNR, require the original image, and cannot fully evaluate the influence of watermark on content. WMarkGPT predicts th... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and recognition of our innovation, comprehensive experiments, superior performance and practical significance. We appreciate that you highlighted WMarkGPT’s ability to predict watermark visibility without the original images, the newly constructed datasets, our pr... | Summary: This paper constructs datasets comprised of watermarked images with different level of watermark visibility. The paper trains a model specified for describing watermark patterns and evaluating watermark visibility level. The authors compare their model with several existing multimodal large language models on ... | Rebuttal 1:
Rebuttal: Thanks for your insightful feedback and recognition on our clear presentation, comprehensive comparisons and better results.
**Q1: The explanation of the "real" dataset**
**Answer:**
Compared to our WQA-Synthetic dataset, which employs pseudo watermarking processes to generate watermarked imag... | Summary: This paper proposed use MLLM to detect watermarked images, the model architecture is adapted from mplug-owl 2 and proposes three training stages to progressively fine-tune model. Experimental results show improved performance.
Claims And Evidence: The paper may overstate the performance gain, i.e., Section 4.... | Rebuttal 1:
Rebuttal: Thanks for your valuable comments and recognition on our clear presentation, contributions of extending the MLLMs ability to watermarked image understanding.
**Q1: Experimental setup of baseline models**
**Answer:**
We clarify that all baseline models were fine-tuned on our proposed datasets ra... | null | null | null | null | null | null |
De-AntiFake: Rethinking the Protective Perturbations Against Voice Cloning Attacks | Accept (poster) | Summary: The paper investigates the effectiveness of adversarial perturbations as a defense against voice cloning (VC) under threat models that specifically considering perturbation purification techniques used by attackers. The study finds that while existing purification methods can reduce the impact of protective pe... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
# Weaknesses
>W1. The proposed method increases time cost due to the introduction of the Refinement stage.
We agree that... | Summary: This paper investigates the vulnerabilities of protective perturbation-based voice clone (VC) defenses, demonstrating that these defenses are susceptible to existing adversarial purification techniques. Additionally, the authors propose an enhanced two-stage adversarial purification method that mitigates embed... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
# Weaknesses
>W1. The process of purification, specifically unconditional diffusion, requires further explanation. For in... | Summary: The paper evaluates limitations of existing purification methods in countering adversarial perturbations designed to block unauthorized voice cloning (VC), revealing they cause feature distortions that degrade VC performance. A novel two-stage purification method is proposed, combining perturbation removal wit... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
# Weaknesses
>W1. The purification method employed is relatively outdated, specifically an unconditional DiffWave model. ... | Summary: Some works claim that an individual can protect their audio samples from voice cloning via perturbations that induce odd behavior from the voice cloning model (generative), or from voice classification models (discriminative). Another set of works show that these defensive perturbations can be removed at least... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
# Claims And Evidence
>Concern about subjective 'human' notation (H). Embedding proxy may not reflect human perception. S... | Summary: The paper "De-AntiFake" systematically evaluates current voice cloning defense mechanisms that use protective perturbations, revealing their vulnerability to adversarial purification techniques. To demonstrate this vulnerability, the authors propose a novel two-stage purification method called PhonePuRe that c... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful and valuable comments. We respond to each comment as follows and sincerely hope that our rebuttal could properly address your concerns.
# Weaknesses
>W1. More discussion about practical limitations.
Our implementation utilized an RTX A6000 GPU and Xeon Gol... | null | null | null | null |
Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction | Accept (oral) | Summary: The paper analyzes the effectiveness of multi-token prediction in open-ended algorithmic tasks that require combinatorial and exploratory creativity. It argues that transformers trained with next-token prediction struggle in these tasks, whereas transformers using multi-token prediction or diffusion models per... | Rebuttal 1:
Rebuttal: Thank you for your time and effort in evaluating the paper! We are excited that you appreciate the analogy between wordplay and our tasks! We are also happy that you found (a) the tasks well-designed, (b) hash-conditioning interesting and (c) did not find any major drawbacks with the work!
We res... | Summary: This paper focuses on addressing the issue of insufficient creativity in traditional next-token prediction (NTP) when applied to open-ended algorithmic tasks. The authors have designed a series of minimalistic algorithmic tasks (such as Sibling Discovery, Triangle Discovery, Circle Construction, and Line Const... | Rebuttal 1:
Rebuttal: Thank you for your time & insightful feedback! We address your points below:
### Major points
> The mechanism underlying the role of hash-conditioning has not been clearly elucidated
Here's our current intuition which we'll add to the paper.
Recall that hash-conditioning explicitly injects ra... | Summary: In this work, the authors aim to study the failure of the next-token prediction (NTP) objective at open-ended creative tasks, where the goal is to generate a diversity of outputs satisfying some constraint. Motivated by the fact that many such tasks require learning to form a latent plan that is not captured f... | Rebuttal 1:
Rebuttal: Thank you for your detailed and encouraging feedback. We are happy that you find our approach elegant and timely given how it is near-impossible to rigorously measure creativity in the real-world. We are also pleased that you find (a) our proposed hash-conditioning approach interesting (b) our cla... | null | null | null | null | null | null | null | null |
Overcoming Non-monotonicity in Transducer-based Streaming Generation | Accept (poster) | Summary: * The paper introduces MonoAttn-Transducer, which enhances the handling of non-monotonic alignments in streaming generation by incorporating monotonic attention.
* The approach leverages the forward-backward algorithm to infer posterior alignment probabilities, enabling efficient training without enumerating e... | Rebuttal 1:
Rebuttal: **Thank you for your thoughtful review! We will make every effort to respond to your concerns.**
>***1. However, the paper could be improved by providing more detailed theoretical analysis of why the proposed method works well, especially in comparison to other methods. Additionally, while the ex... | Summary: This paper investigates an interesting problem of non-monotonic alignment between input/output in streaming generation settings (e.g., simultaneous translation). The solution is to use the forward-backward algorithm to estimate alignment. Results show superior performance on speech-to-text and speech-to-speech... | Rebuttal 1:
Rebuttal: **Thank you for your acknowledgement of this work. We will make every effort to address your remaining concerns.**
>***1. For Table 2., why would MonoAttn still out-perform Transducer with an infinite chunk size? I would anticipate that in such settings, alignment is not as important, as the cont... | Summary: This paper introduces MonoAttn-Transducer to tackle streaming generation tasks. MonoAttn-Transducer adds monotonic attention mechanism on top of Transducer and uses forward-backward algorithm to infer the alignment, which is then used to compute expected context representations used in monotonic attention. Exp... | Rebuttal 1:
Rebuttal: **Thank you for your thoughtful review! We will make every effort to respond to your concerns.**
>***1. Concerns regarding latency metric that are biased toward over-generation:*** *The AL in Table 3 is abnormal, both 118 and 153 ms are abnormally low, which I find hard to believe. One possible c... | Summary: This paper proposes Mono-Attn-Transducer, a streaming sequence model that combines Transducer and monotonic attention. A novel training procedure that utilizes approximate alignment posteriors and alignment priors made training possible without expensive enumeration over an exponential search space or the use ... | Rebuttal 1:
Rebuttal: **Thank you for your efforts in reviewing! We will make every effort to respond to your concerns.**
>***1.However I would need some more information from the authors before I could make a good assessment.***
**We will address your questions about the method point by point.**
1. >*It appears tha... | null | null | null | null | null | null |
LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models | Accept (poster) | Summary: This paper presents a novel dataset, designated as LAION-C, where the letter C denotes "corrupted." This dataset bears similarities to ImageNet and ImageNet-C.
The dataset under consideration contains six corruptions (Mosaic, Glitched, Vertical Lines, Stickers, Geometric Shapes, Luminance Checkerboard) and 16... | Rebuttal 1:
Rebuttal: Dear Reviewer,
Thank you for your favourable review. We appreciate that you find the utilization of LAION-C **logical** because it **poses a new challenge for OOD-evaluation**, and **value the human experiment**. Prompted by your feedback, we will update the manuscript to include a section motiva... | Summary: This paper proposed a new dataset called LAION-C for evaluating image classification model robustness against out-of-distribution data. The LAION-C datasets select samples from ImageNet validation set, and added with 6 different types of synthetic distortions, each with 5 different levels of severity. A substa... | Rebuttal 1:
Rebuttal: Dear Reviewer,
thank you for taking the time to provide such detailed and insightful feedback. We were delighted to read that you found our **experimental design [to be] in general thoughtful and comprehensive**, and appreciated the **substantial evaluation and insightful error analysis**. We ha... | Summary: This paper introduces LAION-C, a new benchmark dataset for evaluating out-of-distribution (OOD) robustness of web-scale vision models. The authors argue that existing benchmarks like ImageNet-C are no longer sufficiently OOD for models trained on massive web datasets like LAION, as these models are likely expo... | Rebuttal 1:
Rebuttal: Dear Reviewer,
thank you for your insightful comments! We are delighted that you find our work to have a **good experimental design** and consider it **relevant to the community**.
*Q: “More ImageNet OOD variants could have been studied / compared with”*
A: Thanks for this excellent suggesti... | null | null | null | null | null | null | null | null |
UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation | Accept (poster) | Summary: This paper proposes a dataset of human and animal images, and their keypoints, bounding boxes, and fine-grained captions. The dataset includes 786K images with 2.9M instances, averaging 3.66 instances per image. The annotations are obtained from the best among several candidates, different annotations having d... | Rebuttal 1:
Rebuttal: **Dear Reviewer u8PW**,
Thank you for your review and constructive comments. During the rebuttal period, we have made every effort to address your concerns. The detailed responses are below:
> Q1: Why human shows mild improvement compared with ControlNet?
>
While our method shows only mild imp... | Summary: The paper proposes a DiT based framework UniMC for keypoint guided multi-instance image generation and introduces HAIG-2.9M dataset designed for keypoint-guided human and animal image generation. Experiments are conducted on COCO, APT36K, and HAIG-2.9M datasets to show the efficacy of the proposed method.
Cla... | Rebuttal 1:
Rebuttal: **Dear Reviewer Rf97,**
Thank you for your review and constructive comments. During the rebuttal period, we have made every effort to address your concerns. The detailed responses are below:
> Q1: The need for a joint human-animal keypoint-annotated dataset.
>
As shown in **Table 4** and **Fig... | Summary: This paper proposes a framework (UNIMC) that generates images containing multiple objects (including humans and various other entities) by leveraging joint keypoints and introduces a large-scale dataset (HAIG-2.9M) to support this approach. Unlike conventional keypoint-based image control methods, UNIMC utiliz... | Rebuttal 1:
Rebuttal: **Dear Reviewer 2vGV,**
Thank you for your review and constructive comments. During the rebuttal period, we have made every effort to address your concerns. The detailed responses are provided below:
> Q1: Applicability to other models.
Our method is designed to be compatible with **almost all ... | Summary: The paper introduces UNIMC, a unified Diffusion Transformer framework for keypoint-guided multi-class image generation, and HAIG-2.9M, a large-scale dataset with 786K images and 2.9M instance annotations covering humans and 30 animal classes. UNIMC addresses limitations in existing approaches by using explicit... | Rebuttal 1:
Rebuttal: **Dear Reviewer kiWJ,**
Thank you for your review and constructive comments. During the rebuttal period, we have made every effort to address your concerns. The detailed responses are provided below:
> Q1: Details about the computational requirements of UNIMC compared to baseline methods.
>
Ea... | null | null | null | null | null | null |
Exploring Invariance in Images through One-way Wave Equations | Accept (poster) | Summary: This paper draws connections between recurring regression using first-order norm + linear autoregression and the discretized one-way wave equation. Through this framework the paper proposes a model to embed images and provides empirical evidence supporting the model performance for image reconstruction. Moreov... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's valuable feedback.
$\color{blue}{[Question-1]:}$
**Can you provide more detailed theoretical insights or formal proofs that justify the assumption that images naturally conform to a one-way wave equation in the latent feature space?**
We acknowledge the challen... | Summary: The authors introduce a new image encoder–decoder architecture called First Order Norm + Linear Autoregression (FINOLA). They compare it with several other image representations—including the Discrete Cosine Transform, the Discrete Wavelet Transform, and various generative models—and report that FINOLA achieve... | Rebuttal 1:
Rebuttal: We are grateful for the reviewer's valuable feedback.
$\color{blue}{[Question-1]:}$
**Contradiction Between Lines 260 and 098: Does the assertion that $\bf{AB}$$^{-1}$ is not diagonalizable conflict with the assumption that is diagonal (line 102 and Equation 5), especially when Figure~15 shows f... | Summary: This paper introduces an encoder-decoder framework that autoregressively reconstructs images using a first-order difference equation. The method achieves high-fidelity reconstruction, outperforms traditional encoding techniques, and is effective for self-supervised learning. The key contribution of the paper i... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback and valuable suggestions, which have significantly improved the quality of our paper.
$\color{blue}{\textbf{[Question 1]:}}$
**While ImageNet is a comprehensive dataset, could the authors evaluate FINOLA on other datasets (e.g., ADE20K,... | Summary: This paper explores the invariance in images and proposes an encoder-decoder framework based on the first-order wave equation. It works by encoding each image into an initial condition vector and then passing it to a special decoder that transforms the first-order wave equation into a linear autoregressive pro... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thoughtful feedback and valuable suggestions, which have significantly improved the quality of our paper.
$\color{blue}{\textbf{[Weakness 1]:}}$
**This method requires a lot of computing resources, especially when the feature map is high resolution. Theref... | null | null | null | null | null | null |
Janus: Dual-Server Multi-Round Secure Aggregation with Verifiability for Federated Learning | Accept (poster) | Summary: The paper proposes a new secure aggregation protocol for federated learning. This protocol relies on two non-colluding servers: one to aggregate masked gradients and the other aggregating one-time-pad masks. Compared to other similar schemes, the masks here are not secret-shared with a graph of neighbours, the... | Rebuttal 1:
Rebuttal: Dear Reviewer nXLy,
Thank you for your valuable feedback and suggestions. Below, we address each of your concerns.
**1. Definition of SHC (W1&Q1):**
The output of the Commit algorithm is c, but c is not a simple tuple. In fact, we intend to convey that the complete commitment can be split into... | Summary: This paper aims to address the challenges in existing secure aggregation (SA) schemes, including scalability with dynamic user participation, vulnerability to model inconsistency attacks (MIA), and the lack of verifiability in server-side aggregation results. The motivation is compelling and beneficial to the ... | Rebuttal 1:
Rebuttal: Dear Reviewer LvMf,
We sincerely appreciate your valuable feedback. Below we provide a point-by-point response.
**1. Dropout rate (W1&Q1):**
To ensure a fair comparison, it is crucial to recognize that mask-based approaches (including BBSA, VeriFL, and Flamingo in Section 4.2) fundamentally re... | Summary: This paper proposes Janus, a secure aggregation scheme based on dual servers for FL, whose core innovation lies in breaking through the communication constraints of the traditional single-server architecture: through the design of a bidirectional interaction protocol that supports multiple rounds of aggregatio... | Rebuttal 1:
Rebuttal: Dear Reviewer uPUn,
Thank you for your valuable comments. We address your concerns as follows.
**1. Dynamic Engagement (W1):**
Our scheme enables dynamic participation where clients can join or leave at any time without compromising security. The process is designed as follows: new clients sim... | Summary: Janus proposes a 2-server aggregation protocol in which one of the servers provides the aggregated masked results and the other the aggregated masks and aggregated commitments so that clients can check that the results are consistent. The protocol also prevents the server from ever learning the output to impro... | Rebuttal 1:
Rebuttal: Dear Reviewer e8s4,
We sincerely appreciate your time and effort spent on our work. We have noticed that most of your concerns stem from misunderstandings about our paper. Below, we will first clarify these misunderstandings point by point.
**Misunderstanding 1. Security assumptions (W1&Q1):** ... | null | null | null | null | null | null |
Learning Robust Neural Processes with Risk-Averse Stochastic Optimization | Accept (poster) | Summary: This paper proposes a training method for robust neural process based on risk-averse stochastic optimization.
Claims And Evidence: Yes.
Methods And Evaluation Criteria: Mostly make sense, except the performance metrics for the experiment part.
Theoretical Claims: No.
Experimental Designs Or Analyses: Yes. ... | Rebuttal 1:
Rebuttal: Thanks for your positive and valuable feedback. We have made efforts to address your concerns. If there are any further questions, please let us know and we will reply promptly.
**Q1.** **The paper uses simple regret as the primary performance measure, but a more robust metric like the CVaR of ... | Summary: This paper investigates the robust neural processes problem from a risk-averse perspective, aiming to control the expected tail risk at a given probabilistic level. The authors formulate the CVaR optimization as a distributionally robust optimization (DRO) problem and propose a double-loop stochastic mirror pr... | Rebuttal 1:
Rebuttal: Thanks for your positive and valuable feedback. We have made efforts to address your concerns. If there are any further questions, please let us know and we will reply promptly.
**Q1.** **The contribution seems incremental.**
> **Reply**: While CVaR optimization is well-established (e.g., in ... | Summary: This paper introduces a new framework for improving the robustness of Neural Processes. Traditional NPs optimize for average performance across tasks using empirical risk minimization, but this can lead to poor adaptation on difficult or high-risk tasks.
The authors propose a risk-averse optimization strategy... | Rebuttal 1:
Rebuttal: Thanks for your positive and valuable feedback. We have made efforts to address your concerns. If there are any further questions, please let us know, and we will reply promptly.
**Q1.** **Ablation experiments on CVaR α, inner/outer loop iteration lengths, and learning rate.**
> **Reply**: We... | null | null | null | null | null | null | null | null |
Efficient Parallel Training Methods for Spiking Neural Networks with Constant Time Complexity | Accept (poster) | Summary: This paper introduces a novel Fixed-point Parallel Training (FPT) method to accelerate Spiking Neural Networks (SNNs) training.
The method is theoretically analyzed for convergence, and the authors show that existing parallel spiking neurons are special cases of this approach.
Experimental results demonstrate ... | Rebuttal 1:
Rebuttal: We sincerely thank you for your high evaluation of our work, seeing this paper as a milestone due to its potential to enable parallel pre-training of SNNs. We also appreciate your valuable and detailed feedback. Below, we will answer your questions.
**Q1: The usage of "\cite" and "\citet"**
**A1... | Summary: This work introduces Fixed-point Parallel Training (FPT), a novel method that reduces SNN training time complexity from O(T) to O(K) (where K is a small constant, typically K = 3) by enabling efficient parallel processing across all timesteps without modifying the network architecture. A theoretical convergenc... | Rebuttal 1:
Rebuttal: We thank you for your encouraging feedback and for recognizing that FPT significantly accelerates SNN training, making it a scalable and efficient solution for large-scale, long-duration tasks. We also appreciate your recognition of its potential in practical applications, especially neuromorphic ... | Summary: The paper proposes a new training method for SNNs called Fixed-point Parallel Training (FPT), which aims to improve efficiency by reducing time complexity from O(T) to O(K), where K is a small constant. The method leverages a fixed-point iteration framework to enable parallel computation across timesteps rathe... | Rebuttal 1:
Rebuttal: We sincerely thank you for recognizing that FPT enhances the efficiency of SNN training without modifying the underlying network architecture and for acknowledging FPT as a novel and well-motivated approach. Below, we provide detailed responses to your questions.
**Q1: How does FPT compare in mem... | Summary: This paper proposes Fixed-point Parallel Training for efficient training of SNN, which does not change the network architectures. This training mode does not affect the dynamics of LIF neurons and achieves better performance on data with time series information such as DVS.
Claims And Evidence: The three cont... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewers for recognizing the novelty and contributions of our proposed FPT framework, including its ability to reduce the training complexity of SNNs and the fact that existing parallel spiking neuron models can be viewed as special cases of FPT. We also appreciate the cons... | null | null | null | null | null | null |
ReFrame: Layer Caching for Accelerated Inference in Real-Time Rendering | Accept (poster) | Summary: This paper incorporates traditional caching methods previously used in U-Net based diffusion models into modern real-time rendering applications. The authors propose a novel caching policy that leverages motion vectors in graphics rendering pipeline to adaptively perform cache updates when the difference betwe... | Rebuttal 1:
Rebuttal: Thank you for the detailed and thoughtful review. We appreciate the advice for Table 1 and will update accordingly. The baseline is used as the ground truth reference and the metrics are computed compared to this reference (i.e. MSE = $\frac{1}{N_{pixels}} \sum_{i=0}^{N_{pixels}-1} (pixel^{baselin... | Summary: The paper proposed a training-free intermediate features caching method to accelerate diffusion model inferencing in real-time rendering. Targeting encoder-decoder style networks, the proposed method caches intermediate network layer outputs to be reused in subsequent inferences in order to reduce frame render... | Rebuttal 1:
Rebuttal: We thank the reviewer for the helpful comments and will update Table 1 as suggested.
Addressing reviewer questions:
*Memory overhead:*
Scenes with a higher resolution will require more memory. However, the cache only stores values of one or a few tensors, which will not cause a huge occupation ... | Summary: The paper proposes to speed up real-time rendering tasks by leveraging the feature caching technique proposed in DeepCache, with extension to UNet++ and adaptive cache polices that are more suitable in the rendering context. Results show 1.4x speed up on average with negligible quality loss.
## update after r... | Rebuttal 1:
Rebuttal: Thank you for the helpful review and feedback on our paper.
To clarify key points:
1. *Testing sequence length:*
We tested short sequences because the cache is only valid for a few frames, and the test of 10-20 frames already includes several cache refreshes. However, we add additional data for ... | Summary: The authors propose a technique for caching intermediate features of U-net style networks to skip computation of hidden layers. These intermediate features are recomputed when the changes is above a certain threshold. Overall, this produces an average improvement of performance without significant drop in qual... | Rebuttal 1:
Rebuttal: Thank you for the helpful review and feedback on our paper.
We have added additional data on the 95th percentile as suggested, reported as runtime in milliseconds.
| **Workload** | **95th Percentile Latency (with cache)** | **95th Percentile Latency (baseline, no cache)** |
| --... | null | null | null | null | null | null |
Activation Space Interventions Can Be Transferred Between Large Language Models | Accept (poster) | Summary: The authors study a learned mapping between the activations of a source and target model, where generally the source will be smaller. They study the extent to which activation-level interventions on the source model can be directly mapped into activation interventions on the target model. They examine backdoo... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our novelty (“I think this is an original and interesting contribution”) and providing helpful comments. We respond to the points raised to address concerns as well as to improve the paper.
1. Paper clarity:
* Section 6/7 reorder; Figure 2 prod/dev fix; Squ... | Summary: This paper investigates how well activation interventions (such as activation steering) transfer across language models (e.g., llama 3.2 1B vs. llama 3.2 3B). They find that models often represent the same high level concepts and that to some extent a simple map (autoencoder or affine transformation) can be en... | Rebuttal 1:
Rebuttal: We thank the reviewer for appreciating our motivation and providing very helpful comments. We would like to respond to the points raised to address concerns as well as to improve the paper.
1. General Claims: Thank you for pointing this out. See response to reviewer ort7 (paper cleanup).
* Gener... | Summary: This paper investigates the ability to transfer activation-space interventions between models by learning a mapping between two models' activation spaces. Both sparse auto-encoders (SAEs) and affine maps are considered as mapping functions for this purpose. They find that it is possible to effectively map stee... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging our results (“the evidence for representation transfer between differently sized models from the same family is strong”) and appreciating our experimental design (“The experimental design looks generally valid.”) as well as providing some very helpful commen... | Summary: This paper proposes a method to transfer interventions on activations between models. Specifically, they train an autoencoder or a linear map to transfer activations from one LLM to another LLM. This enables transfer of capabilities (jailbreaking) and even data distributions (finetuning vs. base). Moreover, th... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the validity of our work (“The results show that the method is somewhat performant.”) and providing very helpful comments. We respond to each of the points raised in order to address the concerns and improve the paper.
1. Paper cleanup
* Revised contribution... | null | null | null | null | null | null |
Efficient Bisection Projection to Ensure Neural-Network Solution Feasibility for Optimization over General Set | Accept (poster) | Summary: The paper introduces a bisection procedure to achieve feasibility of solution outputs from neural networks applied to constrained optimization problems.
## update after rebuttal:
I updated my recommendation following the discussion period.
Claims And Evidence: The paper provides a bisection procedure, establ... | Rebuttal 1:
Rebuttal: **Response**:
Thank you for reviewing our paper. We appreciate your feedback and the chance to address your concerns.
We believe our application of the bisection procedure to neural networks, while simple, offers a practical solution to feasibility challenges in constrained optimization. The sim... | Summary: One of the main challenges of ML-based solution generation in constrained optimization is to ensure feasibility. This paper describes a method to produce feasible solutions that are close to ML-generated potentially-infeasible solutions for compact constraint sets with non-empty interiors, with a focus on effi... | Rebuttal 1:
Rebuttal: We appreciate the recognition of our method's practical value for ML-based solution generation in constrained optimization problems, its theoretical foundation, and strong computational results. Below, we address specific points raised in the review.
---
> `C1: Ability to predict low-eccentricity... | Summary: The paper proposes a technique that enables projecting the outputs of neural nets onto arbitrary compact sets. The goal is for the neural net outputs to be feasible with respect to constraints that are often found in convex and non-convex optimization settings. The approach adopted relies on an efficient bisec... | Rebuttal 1:
Rebuttal: We appreciate the recognition that our approach balances efficiency and optimality while improving over previous work, and that the simplicity of our method adds to its appeal. Below, we address each specific point raised in the review.
---
> `C1: Assumptions on the objective function and optima... | null | null | null | null | null | null | null | null |
PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models | Accept (poster) | Summary: This paper describes PyTDC, an open-source software platform for training, evaluation, and use of biological foundation model. This provides API access to multimodal biological data-sets, a variety of tasks and associated metrics, and APIs for model retrieval and deployment. The utility of this system is demon... | Rebuttal 1:
Rebuttal: Reviewer ckR1,
Thank you very much for the time dedicated to reading the paper and providing your review. Addressing your points below:
--
"The paper describes a useful system. The key weakness is a lack of scientific novelty, which makes it more suited to a systems-focused conference or workshop.... | Summary: This paper introduces PyTDC, an open-source machine learning platform designed to support the training, evaluation, and inference of multimodal biological AI models, with a strong focus on the single-cell domain. PyTDC facilitates integration of diverse biological data sources from single-cell gene expression,... | Rebuttal 1:
Rebuttal: **However, the necessity for adopting the specific 'API-first' architecture and Model-View-Controller (MVC) design pattern are underexplained. While these approaches are well-established in software engineering, the paper does not clearly justify why they are uniquely advantageous for this use cas... | Summary: The paper introduces **PyTDC**, a multimodal machine learning platform designed for training, evaluation, and inference of biomedical foundation models. The platform aims to address the limitations of existing biomedical benchmarks by providing end-to-end infrastructure for integrating multimodal biological da... | Rebuttal 1:
Rebuttal: Dear Reviewer exTK, thank you very much for your time dedicated to reviewing our work. Including responses to your queries below.
**The only concern for me is that the target of PyTDC is supporting "biomedical foundation models", which may be somewhat overclaiming. Biomedicine encompasses a vast ... | Summary: PyTDC is introduced as a cutting-edge multimodal machine learning infrastructure designed to streamline the training, evaluation, and inference of biomedical foundation models. By unifying heterogeneous, continuously updated data sources and providing a model server for seamless access to pre-trained models an... | Rebuttal 1:
Rebuttal: Dear Reviewer ATdV,
Thank you very much for your time dedicated to reviewing our work. Including our responses to your queries below.
**PyTDC doesn't include text data, yet natural language is a key modality in biomedicine. Is it appropriate to claim comprehensive support for biomedical foundatio... | null | null | null | null | null | null |
CoreMatching: A Co-adaptive Sparse Inference Framework with Token and Neuron Pruning for Comprehensive Acceleration of Vision-Language Models | Accept (poster) | Summary: CoreMatching combines token sparsity and neuron sparsity through the interaction between core neurons and core tokens, achieving comprehensive acceleration of VLMs. The proposed projection-guided criterion provides a more accurate way to evaluate token importance, and the co-adaptive framework effectively redu... | Rebuttal 1:
Rebuttal: Dear Reviewer cD4w,
We would like to thank you for taking the time to review our paper and provide valuable feedback. We appreciate the opportunity to address your questions and concerns. We believe these discussions and revisions will help further improve the paper.
***Q1. Is this technique com... | Summary: The authors explore a fundamental question on jointly leveraging token and neural sparsity to enhance the inference efficiency of vision-language models (VLMs). The paper introduces the concept of core neurons and investigates their correspondence with core tokens. Building on this observation, the authors pro... | Rebuttal 1:
Rebuttal: Dear Reviewer T5zD,
Thank you for your thoughtful review and encouraging feedback. We're pleased you found the paper well-motivated and well-structured, and we welcome the opportunity to address your comments to further improve our work.
***Q1. How to determine these two hyperparameters to balan... | Summary: This paper proposes CoreMatching, combining the sparsity of core neurons and core tokens for VLMs. Experiments show that CoreMatching excels in both model accuracy and inference efficiency.
Claims And Evidence: The claims (i.e., model accuracy and deployment efficiency) are clear. Theoretical analysis and eff... | Rebuttal 1:
Rebuttal: Dear Reviewer LK6j,
We sincerely thank you for taking the time to thoroughly read our paper and for your positive evaluation. We are excited to have the opportunity to address your questions and concerns. These discussions and revisions will further strengthen our work.
***Concern 1. Lack of acc... | Summary: This paper proposes a sparse inference framework to reduce the inference latency of Vision-Language Models (VLMs). The main method combines token compression and neural unit compression techniques, and establishes a connection between the two. Through the CoreMatching approach, it is possible to significantly ... | Rebuttal 1:
Rebuttal: Dear Reviewer TfEt,
We sincerely thank you for taking the time to review our paper and for providing valuable feedback. We are pleased to hear that you found both our theoretical analysis and experimental validation to be comprehensive. We appreciate the opportunity to address your suggestions, w... | null | null | null | null | null | null |
Positive-unlabeled AUC Maximization under Covariate Shift | Accept (poster) | Summary: In this paper, the authors focus on addressing the problem of AUC maximization under distribution shifts between training and testing data, specifically under the scenario of covariate shift, where the input distributions of the training and test data differ, but the conditional distribution of the class label... | Rebuttal 1:
Rebuttal: Thank you for your positive comments and constructive feedback.
We added experimental results in the anonymous URL: https://anonymous.4open.science/r/icml25s-6C74/results.pdf
> Weaknesses: (1) The paper lacks a clear explanation of the motivation for deriving the two estimators of the AUC risk on... | Summary: The paper addresses the problem of maximizing the AUC in binary classification tasks under covariate shift, where the input distribution changes between training and test phases, but the conditional distribution of the class label given the input remains the same. The authors propose a novel method that levera... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments and constructive feedback.
We added experimental results in the anonymous URL: https://anonymous.4open.science/r/icml25s-6C74/results.pdf
> The scale of the adopted datasets is too small, e.g., only "50 positive and 3,000 unlabeled data in the training distr... | Summary: This paper proposes a new method for AUC maximization under covariate shift using positive-unlabeled (PU) data from the training distribution and unlabeled data from the test distribution. Given the challenges of estimating class priors of the training and test distribution, this paper theoretically derives tw... | Rebuttal 1:
Rebuttal: We appreciate your positive comments on our paper. | Summary: The paper aims to optimize the AUC under a covariate shift, i.e., when the test distribution of inputs differs from the training distribution. Considering the difficulty of collecting negative examples, this work focuses on the positive-unlabelled (PU) setting. To solve this problem, the paper first proposes t... | Rebuttal 1:
Rebuttal: Thank you for your positive and constructive comments.
We will revise the paper according to your suggestion regarding the structure and notation.
We added experimental results in the anonymous URL: https://anonymous.4open.science/r/icml25s-6C74/results.pdf
> the benchmark datasets could be furth... | null | null | null | null | null | null |
Fixed-Confidence Multiple Change Point Identification under Bandit Feedback | Accept (poster) | Summary: This paper investigates the problem of identifying multiple change points in a piecewise constant function under bandit feedback, ensuring a fixed level of confidence in the results.
The authors consider two scenarios: 1) known number of $N$ change points, and 2) unknown number of change points ($m \geq N$).
... | Rebuttal 1:
Rebuttal: Thank you for your review of our work. We appreciate that you found the claims in the paper clear, that the theoretical claims were strong and well-reasoned, and that the experimental results were supportive of our theoretical contributions. We also are hopeful that the proposed methods will contr... | Summary: Goal is to identify with high confidence the change points in the mean reward of the actions across the action space, by using as few samples as possible. A new Track-and-Stop algorithm is proposed for this task with asymptotic optimality. Matching lower-bounds are also proven.
-- update after rebuttal --
Th... | Rebuttal 1:
Rebuttal: Thank you for the detailed review and for your constructive questions. We are glad that you found the paper well-written and that you think our work would be interesting for the bandits/change point community. We respond to your comments below.
---
**Other Strengths and Weaknesses:**
We will in... | Summary: This work introduces a fixed-confidence piecewise constant bandit problem, and provides instance-dependent lower bounds for the complexity of change point identification in this problem. This work also devises a computationally efficient algorithm as a variant of track-and-stop and prove its asymptotic optimal... | Rebuttal 1:
Rebuttal: We thank the reviewer for their detailed, constructive comments and we are glad that you found the problem novel. We provide some responses to your comments below.
---
**Related Literature:**
You are correct that most of the literature on bandits with change points is focused on non-stationary ... | null | null | null | null | null | null | null | null |
Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups | Accept (poster) | Summary: The paper presents theoretical developments to learning the infinitesimal generators of markov semigroups using Laplace transform.
Claims And Evidence: The paper's claims all theoretically substantiated. Empirical evidence backs the claims, but the experiments are of very small scale.
Methods And Evaluation ... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback. Due to space constraints, we address the key issues and questions briefly, committing to incorporate all suggestions in our revision. Further details are available upon request.
# Empirical evidence:
For our additional [results](https://green-chantal-92.tii... | Summary: The authors present an approach for learning continuous Markov semigroups. Notably their approach comes with theoretical guarantees at any time-lag. In addition, their approach scales linearly in the state dimension opening the door to apply their methods on high-dimensional problems. Finally they demonstrate ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful evaluation and valuable comments. Below, due to space limitations, we briefly address the highlighted weaknesses and respond to the reviewer’s questions, committing to incorporating all feedback in our revision. If needed, we can elaborate further on each po... | Summary: The paper deals with learning continuous-time Markovian dynamics. While existing methods focus on learning transfer operators, here the authors suggest to learn a spectral decomposition of the semigroup's generator, under some assumptions. This is done by finding a (finite-rank) approximation of the resolvent ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful evaluation and valuable comments. Below, due to space limitations, we briefly address the highlighted weaknesses and respond to the reviewer’s questions, committing to incorporating all feedback in our revision. If needed, we can elaborate further on each po... | Summary: This paper studies a new class of non-parametric learning algorithms for continuous-time Markov processes, specifically for learning the eigenfunctions and eigenvalues of their infinitesimal generator (IG) of the semigroup of transfer operators (TO). While existing methods tend to focus on learning the TO whic... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s insightful evaluation and valuable comments. Below, due to space limitations, we briefly address the highlighted weaknesses and respond to the reviewer’s questions, committing to incorporating all feedback in our revision. If needed, we can elaborate further on each po... | null | null | null | null | null | null |
A Variational Framework for Improving Naturalness in Generative Spoken Language Models | Accept (poster) | Summary: This paper proposes a variational approach to speech-language modelling in contrast to traditional auto-regressive models. The aim is to capture information other than semantics.
## update after rebuttal
I checked the results in Appendix H and I think the results are interesting. Why not add those results int... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing both the robustness of our experimental design and the novelty of our contribution. Below, we address each concern raised:
## Emotion and Speaker Recognition
> My main concern is how this method is useful for more practical downstream tasks such as... | Summary: This paper automatically learns continuous speech attributes (such as pitch, energy, spectrum) through VAE, and jointly model them with semantic tokens to improve the naturalness and language fidelity of generated speech. Experiments show that this method significantly outperforms the baseline model in subject... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the thorough review and acknowledging the strengths of our paper, including the balance between innovation and practicability, the automatic learning of continuous prosodic features, and our rigorous experimental design.
Below, we address the feedback from the ... | Summary: This paper proposes a variational framework to enhance the naturalness of generative spoken language models by jointly learning continuous paralinguistic features and discrete semantic tokens. Traditional token-based models often neglect prosodic information, leading to unnatural speech. The authors address th... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their constructive feedback. We address your concerns comprehensively below. We present the Tables of new experiment results [here](https://anonymous.4open.science/api/repo/icml-rebuttal-BDD8/file/ICML-rebuttal-1.pdf).
## Comparison with Additional Baselines... | Summary: The authors propose a variational approach that directly encodes desired information from raw audio inputs, addressing the challenge of preserving prosody when modeling discrete speech codes primarily focused on phonetic content (such as HuBERT tokens). Their variational approach shows improved performance com... | Rebuttal 1:
Rebuttal: We appreciate both the recognition of our formulation's correctness and the critical feedback from the reviewer. We address the feedback below.
## Computational Complexity
>The model relies on a diffusion decoder, which increases computational complexity compared to predicting discrete acoustic c... | null | null | null | null | null | null |
A Mixture-Based Framework for Guiding Diffusion Models | Accept (poster) | Summary: This paper explores solving linear and nonlinear inverse problems—sampling from $p(\mathbf{x}_0|\mathbf{y})$—using pre-trained unconditional diffusion models in a Bayesian framework. To approximate the posterior, the authors iteratively sample from intermediate distributions $p(\mathbf{x}_t|\mathbf{y})$, where... | Rebuttal 1:
Rebuttal: Thank you for your thorough review of our paper. We address your main weak points/questions below. The additional tables we discuss below can be found here: https://anonymous.4open.science/r/rebuttal-F9B0/rebuttal_tables.pdf
> **[...] at least one example with a non-Gaussian likelihood [..].**
... | Summary: The paper presents a novel training-free guidance method that allows to samples from g(y|x_0)p(x_0) where p(x_0) is a pre-trained diffusion model distribution and g(y|x_0) is a likelihood function on the clean data. To do this, they come up with a novel approach to approximate the conditioned noisy distributio... | Rebuttal 1:
Rebuttal: Thank you for your thorough review. We address the main weak points/questions below. The additional tables and figures mentioned below can be found here: https://anonymous.4open.science/r/rebuttal-F9B0/rebuttal_tables.pdf
> **comparison of runtime[...]**
Although initially not mentioned (now co... | Summary: To resolve the error in approximated likelihood gradient of diffusion posterior sampling (DPS) and relevant works, the paper defines a novel posterior density $p(x_t|y)$ as mixture of normalized $\hat p_s(x_t|y)= \hat p_s(y|x_t)p(x_t)$ where $\hat p_s(y|x_t)= \int \hat p(y|x_s)p_{s|t}(x_s|x_t) dx_s$ , $\hat p(... | Rebuttal 1:
Rebuttal: Thank you for your feedback. We appreciate your acknowledgment of the paper's clarity and the promising nature of our method. Below, we directly address your key points and questions, supported by additional results. The supplementary tables and figures mentioned can be accessed here: https://anon... | Summary: The paper introduces Mixture-Guided Diffusion Model (MGDM) algorithm to improve likelihood approximation in diffusion models using Gibbs sampling. The approach constructs a mixture approximation of intermediate posterior distributions to address lack of closed-form likelihood scores. Data augmentation scheme u... | Rebuttal 1:
Rebuttal: We thank the reviewer for their constructive comments and helpful suggestions. Below, we directly address the points raised:
>**1. Gibbs sampling can have convergence issues, even if you run more iterations. How do you address it?**
We acknowledge that Gibbs sampling can indeed exhibit convergen... | null | null | null | null | null | null |
Scaling Laws for Pre-training Agents and World Models | Accept (poster) | Summary: The work presents a scaling law study examining the behavior of action and observation prediction models (behavior cloning and world models, respectively). The main results characterize trade-offs between model and dataset scaling given a fixed compute (FLOPS) budget. One result evaluates world model predictio... | Rebuttal 1:
Rebuttal: Many thanks for taking the time to review our paper in such detail, and raising several points we had not considered. We are pleased to see the value of our contributions has been recognized. Below, we respond to your main questions, followed by your ‘other comments’ and finally several points rai... | Summary: This paper investigates the scaling laws in embodied AI. Specifically, this paper focuses on the infinite data regime and generative pre-training objectives, which include behavior cloning and world modeling. The scaling laws are observed in the following two cases through the experiments. One is world modelin... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our paper. Please see below our responses.
- __Comment 1.__ Infinite data regime assumption
Thank you for drawing attention to this important detail. To clarify, all experiments in our paper for both domains (video games and robotics) are conducted in the ... | Summary: This paper investigates the existence and characteristics of scaling laws in embodied AI tasks, specifically world modeling (WM) and behavior cloning (BC), drawing parallels to scaling laws observed in large language models (LLMs). Through extensive experiments on large-scale video game datasets (e.g., Bleedin... | Rebuttal 1:
Rebuttal: Thank you for your review. We are delighted to have successfully communicated the value of our study. We agree that extending principles from LLMs to embodied AI tasks is a timely and important avenue of research. Allow us to respond to your comments below.
- __Comment 1.__ RT-1 dataset has limit... | Summary: The paper explores scaling laws in embodied AI, especially for the pre-training stage of world models and agent behavior. The authors show that power laws similar to those in LLMs are observed in world modeling and behavior cloning, but with coefficients influenced by the tokenizer, task, and architecture. The... | Rebuttal 1:
Rebuttal: Thank you for your review, we are pleased your judgement of the paper comes out on the side of acceptance and agree that your suggestions would further improve the paper. Due to logistical constraints, we are not able to complete all of these requests, but commit to completing the smaller scales B... | null | null | null | null | null | null |
Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models | Accept (poster) | Summary: The paper proposes a model guidance to extract fine-tuning data, that leverages its base pre-trained model as a guidance. The proposed method “model guidance” can sample from the learned distribution of the fine-tuned models via simple guidance techniques. They further propose a new clustering algorithm for sa... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
1. For Weakness1:
> I couldn't find the training data extraction result using the extracted prompt. In my opinion, this part is crucial for highlighting the paper's practical motivation, so the results should be reported.
Our default setting uses captio... | Summary: This paper introduces FineXtract, a framework for extracting data used in the fine-tuning of personalized diffusion models. The authors propose a parametric approach to approximate the fine-tuning data distribution by extrapolating the original output distributions of both the pre-trained and fine-tuned models... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
1. For Questions1:
> It is commendable that the paper discusses strategies for partially extracting and extending captions when they are unavailable. However, did the authors evaluate FineXtract using these extracted captions? If so, how does the performa... | Summary: This paper introduces a novel technique for extracting fine-tuning data from personalized diffusion models, distinguishing it from prior work on data extraction in standard diffusion models. The additional constraints imposed by the fine-tuning phase have real-world implications, enabling more effective data e... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback. We greatly appreciate your positive comments! | Summary: This paper introduces a method to extract training data from personalized diffusion models (DMs). The method approximates the fine-tuned model's distribution as an interpolation between the pretrained model's distribution and the fine-tuning data distribution. By extrapolating the score functions of these mode... | Rebuttal 1:
Rebuttal: Thank you for your insightful feedback!
1. About Experiment Design and Analysis:
> Experiments on model architectures only account for convolution-based diffusion. Current state-of-the-art generative models, like SD3, Flux or Sana, feature a transformer-based architecture. The paper would benef... | null | null | null | null | null | null |
Multiobjective distribution matching | Accept (poster) | Summary: The paper tries to figure out how to do distribution matching to generate a distribution that aligns with multiple underlying distributions, often with conflicting objectives, known as a Pareto optimal distribution. The paper develops a theory on information geometry to construct the Pareto set. This allows fo... | Summary: The paper propose multiobjective distribution matching (MODM) using tools from information geometry. two concrete algorithms are introduced: a multiobjective variational autoencoder (MOVAE) and a multiobjective generative adversarial network (MOGAN). Experiments on the QuickDraw dataset are provided to demonst... | Summary: This paper studies matching a distribution to multiple target distributions. The authors use information geometry to find Pareto optimal solutions, particularly for the exponential family. They apply this to multivariate normal distributions and a MOVAE. They also propose a multi objective GAN. Experiments ... | Rebuttal 1:
Rebuttal: We thanks for your valuable comments and hope that our following response can address some of your concerns.
**W4 The theoretical analysis is restricted to special families of distributions (mainly exponential families like multivariate normals), which limits the generality. Some discussions on t... | Summary: The paper develops a theoretical framework for multiobjective distribution matching using information geometry, deriving explicit forms for the Pareto set and Pareto front for exponential family distributions. It applies these insights to design multiobjective generative models.
Claims And Evidence: The deriv... | Rebuttal 1:
Rebuttal: We sincerely thanks for all valuable comments from you. We thanks for that your believe our theoretical results is strong and hope that our following response can address your concerns more or less.
----
**W1. Limited experimental validation.**
To response with that, we have included a new dat... | null | null | null | null | null | null | ||
Reliable and Efficient Amortized Model-based Evaluation | Accept (poster) | Summary: This Paper proposes a new approach to evaluate LLM performance via IRT and to provide item generation with pre-chosen difficulty levels. I am not an expert in LLMs but I happened to work on IRT in the past. So my point mainly concern this aspect.
I am very short on time for ICML reviews. Apologies for my re... | Rebuttal 1:
Rebuttal: Dear Reviewer Kyh8,
Thank you for your valuable feedback. We answer your comment below.
**Answer to Other Strengths And Weaknesses 1:** When the difficulty (ability) is known, the sum of the responses is indeed a sufficient statistic for ability (difficulty). During calibration, we generally do ... | Summary: The paper introduces a new way to evaluate large language models (LLMs) using Item Response Theory (IRT), a method from psychology that helps measure abilities and item difficulties separately. Traditional evaluation methods can be expensive and depend too much on the specific test questions chosen, so this pa... | Rebuttal 1:
Rebuttal: Dear Reviewer 2sSn,
Thank you for your valuable feedback. We answer your comment below.
**Essential References Not Discussed:** Not much. But it would be better to discuss related topics such as LLM performance prediction. The current related work is rather too short.
**Answer:** Thank you for ... | Summary: This paper proposes a novel amortized model-based approach based on Item Response Theory to tackle the problem of the dependence of evaluation procedures on test subset selection and the high cost of running extensive evaluations. Through extensive experiments, the authors show a reduced query complexity while... | Rebuttal 1:
Rebuttal: Dear Reviewer ztqt,
Thank you for your valuable feedback. We answer your comment below.
**Other Strengths And Weaknesses:** Figures, tables etc aren't properly referenced anywhere throughout the paper, e.g “Figure 2” in line 315 column 2 or “Table 2” in line 809 in Appendix.
**Answer:** Thank y... | Summary: This work proposed a novel way of revisiting large-scale LLM evaluation from IRT perspective. The novel contribution comes from different perspectives: (1) using LLMs to estimate the difficulty of evaluation examples (items), (2) LLM based item generator that can generate a synthetic evaluation example based o... | Rebuttal 1:
Rebuttal: Dear Reviewer ydtz,
Thank you for your valuable feedback. For both PPO training and data generation, we used a temperature of 0.6, top_p of 0.9, and a max_tokens of 256. We have added this information to the updated submission. We appreciate your attention to detail and hope this clarifies our ex... | null | null | null | null | null | null |
Dendritic Localized Learning: Toward Biologically Plausible Algorithm | Accept (poster) | Summary: This work proposes a biologically plausible algorithm for training deep neural networks utilizing apical dendrites. The authors apply the proposed algorithms to learning in MLPs, CNNs, and RNNs, and demonstrate that the proposed algorithm outperforms previous biologically plausible algorithms that satisfy all ... | Rebuttal 1:
Rebuttal: ### 1. Feedback Alignment with MLP on MNIST can achieve 98% test performance.
As stated in Line 325 (left column), to ensure fairness, we adopted the same architecture (784-1024-512-256-10 FC layers, see Appendix C.2) for all algorithms on MNIST.
The architecture used in the paper you mentioned c... | Summary: The article proposes a neural network that is constructed using a local loss and asymmetric weights in the forward and backward passes. The author introduces three criteria for biological plausibility that the neural network should satisfy and demonstrates that the proposed DLL meets these criteria. The author... | Rebuttal 1:
Rebuttal: Thank you for your comments and suggestions, which are valuable for enhancing our paper. We are pleased that our contributions are well recognized.
Responses to your concerns and questions are hereby presented:
### 1. Why are the three criteria for biological plausibility important? More ablatio... | Summary: The paper introduces Dendritic Localized Learning, as an alternative to backpropagation for training neural networks. The goal is to make learning more biologically realistic by addressing three main issues with backpropagation: the requirement of weight symmetry between the forward and backward passes, the us... | Rebuttal 1:
Rebuttal: 1. More experimental validation, especially real-time learning, to show simultaneous forward and backward.
Our approach enables real-time learning, as higher-layer neurons propagate signals backward only when a discrepancy between the output and the label is detected. Otherwise, no adjustments ar... | Summary: The paper introduces Dendritic Localized Learning (DLL), a biologically plausible learning algorithm inspired by the structure and plasticity of pyramidal neurons. The motivation behind DLL is to address three fundamental biological limitations of backpropagation:
- Weight symmetry – Backprop requires symmetr... | Rebuttal 1:
Rebuttal: ### 1. Performance of BP on CIFAR10 with CNN (75%) is low.
Firstly, as stated in Line 325 (left column), to ensure fair comparisons between different algorithms, we used the same architecture for all methods on a given dataset.
Specifically, for CIFAR-10, we employed a CNN with three convolution... | Summary: The paper focuses on biological plausibility. Looking at prior work, this paper proposes three different metrics for biological plausibility including (i) asymmetry between the forward and the backward weights, (ii) local losses, and (iii) non two-stage training. With that, the paper proposes a new learning sy... | Rebuttal 1:
Rebuttal: ### 1. Poorly supported claim of 3 criteria without any citation. There are many other criteria explored in prior work.
Firstly, in Section 2.2, we provide a detailed introduction and evaluation of all representative biologically plausible learning algorithms. These algorithms served as the basis... | null | null | null | null |
False Coverage Proportion Control for Conformal Prediction | Accept (poster) | Summary: The authors propose using the Joint Error Control (JER) framework of Blanchard et al. (2020) to control the false coverage proportion (FCP) across multiple conformal prediction intervals. This approach leverages the exact joint distribution of conformal $p$-values derived in Gavin et al. (2024). They introduce... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
>In Lemma 1, how are ( p_1, \dots, p_m ) defined? Is ( p_j ) equal to ( P(Y_j) ), where ( P(\cdot) ) represents the p-value function?
For each $j \in [[m]]$, we define
$p_j := \frac{... | Summary: The paper introduces CoJER (Conformal Joint Error Rate), a novel method designed to improve the reliability of split conformal prediction (SCP) by controlling the False Coverage Proportion (FCP). While traditional SCP ensures marginal coverage over multiple test points, it does not guarantee that the proportio... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
>However, one potential limitation is that the paper primarily evaluates performance on tabular regression datasets. The applicability of CoJER to classification problems or high-dime... | Summary: This paper examines the limitations of Split Conformal Prediction (SCP) in controlling the False Coverage Proportion (FCP) across multiple predictions. While SCP ensures control over the False Coverage Rate (FCR), it does not provide high-probability guarantees on the actual proportion of non-covered intervals... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
>However, the evaluation setup raises concerns. Since SCP does not ensure FCP control, it should not be included in interval length comparisons [...] including additional baseline met... | Summary: This paper addresses the challenge of controlling the False Coverage Proportion (FCP) in split conformal prediction (SCP). While SCP provides computationally efficient confidence intervals, it only guarantees marginal coverage over multiple test points. The authors highlight that in real-world scenarios, where... | Rebuttal 1:
Rebuttal: We thank the reviewer for their time and insightful comments. Please find our answers to the points raised below.
> The paper is very dense and hard to read. Some toy examples / walk-throughs of the procedure could be quite useful for understanding.The template-building procedure is not described... | null | null | null | null | null | null |
Mixture of Hidden-Dimensions: Not All Hidden-States’ Dimensions are Needed in Transformer | Accept (poster) | Summary: This paper presents a novel Transformer architecture to address the challenges associated with scaling hidden dimensions. The proposed MoHD (Mixture of Hidden Dimensions), leverages the observation of high hidden dimension sparsity to enhance computational efficiency and model performance. In experiments, MoHD... | Rebuttal 1:
Rebuttal: Thank you very much for your high evaluation of our work!
**Q1:** Providing theoretical grounding for MoHD’s sparsity and routing mechanisms.
**R1:** We refer to our response to Reviewer fdJw Q4, where we discuss how sparse mixed activation expands effective width, reduces complexity, and improv... | Summary: - The proposed Mixed Hidden Dimensions (MOHD) aims to address the inefficiency of hidden dimension scaling.
- The core insight lies in the observation that only subsets of dimensions are activated across tokens, with some dimensions shared globally and others allocated as "private" dimensions.
- The MOHD mode... | Rebuttal 1:
Rebuttal: We are greatly encouraged by the reviewer’s positive feedback. Below, we address each of your comments in detail.
**Q1: Theoretical Grounding**
**R1:** Please see our response to **Reviewer fdJw (Q4)** for a more detailed explanation. In summary, we theoretically ground MoHD by showing that:
1... | Summary: This paper proposes a LLM sparsification method, namely Mixture of Hidden Dimension (MOHD), to improve the efficiency of Transformer-based LLMs. Based on the observation that only a small subset of hidden dimensions is shared and activated across tokens in given texts, MOHD selectively discern shared and speci... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and suggestions.
**Q1: Comparison with MoE and other sparsification methods**
**R1:** We respectfully refer to our responses to Reviewer fdJw (Q2, Q3) for a detailed comparison between MoHD and MoE, especially regarding routing design and efficiency. Comparis... | Summary: This paper proposes MoHD (Mixture of Hidden Dimensions), an architecture that optimizes hidden dimension usage via dynamic routing between shared and token-specific dimensions. Experiments demonstrate its superior parameter efficiency and task performance over existing models.
Claims And Evidence: The paper p... | Rebuttal 1:
Rebuttal: Thank you for the valuable comments and suggestions.
**Q1: On MoHD-495M’s performance on WinoGrande (WG)**
**R1:** We respectfully clarify that MoHD-495M does **not consistently underperform** on WG. In fact, our MoHD 50%-495M model achieves **52.7%**, outperforming the **LLaMA2-495M baseline (5... | null | null | null | null | null | null |
Hessian Geometry of Latent Space in Generative Models | Accept (poster) | Summary: ## Update After Rebuttal
## I maintained my score. Please see my response to the reviewer below for my reasons.
----
This work presents a novel technique for analyzing latent space geometry in diffusion models. Based on the reconstruction of the Fisher Information metric, this method approximates the posterior... | Rebuttal 1:
Rebuttal: We highly appreciate provided feedback and agree that the paper will benefit from additional quantitative studies.
- Providing additional quantitative result, like Table (2) of Shao et al. (2018)
Please refer to the General Response below.
- Adding experiments for non-stable diffusion generat... | Summary: This paper proposes a novel method to approximate the Fisher information metric. It shows superior performance to concurrent methods on Ising and Tasep models. Applied to diffusion models, it reveals a fractal structure of the latent space, and sharp transitions. It also allow for smoother interpolations.
## ... | Rebuttal 1:
Rebuttal: We thank the reviewer for highlighting gaps in our evaluation and presentation. The revision will strengthen empirical validation with baselines and metrics and improve clarity via algorithms and reproducibility details. To address concerns about comparison with other methods please refer to table... | Summary: This paper leveraged the information geometry framework to understand the manifold geometry in statistical mechanics models parameter space and generative model latent space.
They provided nice theoretical results connecting the posterior distribution of parameter given infinite samples and the Bregman diver... | Rebuttal 1:
Rebuttal: We thank author for the elaborated questions regarding theory and method.
- The proposed approach seems a bit overly complex, not clear whether it is better than established approach, that is, pulling back the euclidean metric in feature space [Wang & Ponce 2021] [Shao, Kumar, & Fletcher, 2018].
... | Summary: This paper introduces a novel approach to exploring the geometric structure of latent
spaces in generative models by estimating the Fisher metric and investigating phase
transitions. Through theoretical developments and empirical validation on statistical
physics models and Stable Diffusion. Key findings inclu... | Rebuttal 1:
Rebuttal: We thank the reviewer for the feedback and hope that the response below will adress the raised points.
- How confident are you that the proposed Fisher metric reconstruction method can be generalized beyond the specific models?
We are fairly confident it can generalize to other types of gene... | null | null | null | null | null | null |
Conformal Tail Risk Control for Large Language Model Alignment | Accept (poster) | Summary: The paper studies the problem of making sure that the LLM outputs align with human preferences. To this end, they construct an approach where the output of the LLM is returned only if its machine (disutility) score is lower than a "toxicity" threshold. Since machine scores can be different than "true" human sc... | Rebuttal 1:
Rebuttal: We thank the reviewers for their thoughtful feedback. We are encouraged by the recognition of key strengths in our submission, including:
- The **novelty** of the problem setting—controlling tail risk in LLM outputs with distribution-free guarantees;
- The **practical relevance and effectiveness*... | Summary: To avoid the high cost of human annotations, researchers have developed automatic scoring models to assess the tail events produced by LLMs, such as toxic answers.
However, there may be a misalignment between human judgement and model scoring. To solve this issue, this study proposes a lightweight calibration ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer’s thoughtful feedback and are encouraged by the recognition of our paper’s strengths, including:
- The **novelty** of the problem setting—tail risk control in LLM alignment with provable guarantees;
- The **rigor and clarity** of our writing and symbolic notation;
- The... | Summary: This paper focuses on the application of conformal prediction to tail events, which can lead to poor outcomes. The authors propose a lightweight calibration framework for black-box models that ensures alignment between humans and machines with provable guarantees. They utilize L-statistics, the DKW inequality,... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive comments and questions. We are encouraged by the recognition of several key strengths:
- Our **detailed theoretical analysis** and use of L-statistics in conformal risk control;
- The **practical relevance** of our proposed framework, including its light... | Summary: The paper proposes an inference-time alignment procedure to control the risks associated with LLM outputs. It assumes access to a disutility function that can score LLM’s outputs. It works by generating LLM responses until it gets a response that has disutility score below a threshold $\hat{\lambda}$ determine... | Rebuttal 1:
Rebuttal: We thank the reviewer for the thoughtful feedback. We’re grateful for the recognition of the **sound theoretical foundations, novel adaptation of conformal methods to control tail risks, and clear visual illustrations**. Below we address the main concerns:
### **Weakness 3: Additional inference c... | null | null | null | null | null | null |
MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters | Accept (poster) | Summary: This paper proposes to optimize step size by viewing step size as a differentiable parameter, and minimizing the objective of cumulated loss by recording a temporal trajectory.
Claims And Evidence: Yes
Methods And Evaluation Criteria: No. Using eqn (5) as a surrogate of eqn (4) is the basic to make the propo... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's honesty in stating clearly that this paper falls outside their area of expertise. However, it seems there was a critical misunderstanding regarding the approximation in Equation (5).
> **Reviewer:** _"Using eqn (5) as a surrogate of eqn (4) is the basic to make the pr... | Summary: This paper proposes a method for optimising hyperparameters online in first order optimisation algorithms. Using this framework, performance is drastically improved over using fixed hyperparameters in a number of experiments. There are a number of qualitative approximations used which can simplify the complexi... | Rebuttal 1:
Rebuttal: We thank the reviewer for their thoughtful feedback and helpful suggestions, which we address below and plan to incorporate into the final version of the paper. We believe this will significantly enhance the clarity and quality of the work.
---
### Continual Learning
>I think the missing benchma... | Summary: This paper introduces MetaOptimize, an approach for automatically adapting the learning rates of base optimization algorithms like SGD, Adam, and Lion. MetaOptimize maintains a set of learning rates updated with a separate stochastic gradient optimizer to minimize the discounted sum of future losses. Naively... | Rebuttal 1:
Rebuttal: We thank Reviewer wz4K for thoughtful and valuable feedback. Below we provide clarifications, which we will also highlight in final version of the paper.
---
### On Discount Factor γ:
>Authors state γ=1 was used in stationary experiments, and performance meaningfully degrades for γ<0.999, makin... | Summary: The proposed approach seeks to dynamically adjust hyper parameters during the optimization process. The MetaOptimize framework, which utilizes historical iteration data, seeks to minimize regret builds on a discounted sum of future losses. This framework, coupled with several approximations, calculates meta-gr... | Rebuttal 1:
Rebuttal: We thank Reviewer Liw6 for their thoughtful and constructive comments. We carefully considered each suggestion and provide detailed clarifications below. We will apply the suggested improvements in the final version, which we believe significantly helps improve the clarity and presentation of the ... | null | null | null | null | null | null |
Towards Efficient Online Tuning of VLM Agents via Counterfactual Soft Reinforcement Learning | Accept (poster) | Summary: This paper keenly and ingeniously identifies that the influence of tokens on the parsed action varies, with a small subset of action-critical tokens decisively shaping the final outcome. Therefore, after calculating causal weights using Structural Causal Models (SCM) and counterfactors, the authors propose Cou... | Rebuttal 1:
Rebuttal: Thank you very much for your thoughtful review. We sincerely appreciate you pointing out our method's core insight, theoretical soundness, and the credibility of our experimental evaluation. Below, we provide our detailed responses to your remaining questions.
> Q1. What specifically is the dista... | Summary: This paper introduces CoSo, a soft reinforcement learning (RL) method for fine-tuning Visual Language Models (VLMs). CoSo incorporates a per-token weighted entropy regularization term, encouraging exploration on impactful tokens. It is built on two key contributions:
- A counterfactual approach, where generate... | Rebuttal 1:
Rebuttal: Thank you very much for your detailed and thoughtful review. We truly appreciate you highlighting the strengths of our method and thinking it timely, well-discussed, easily applicable, and supported by insightful experiments. Regarding your questions, we provide our responses as follows.
> Q1. Wh... | Summary: This paper investigates the fine-tuning of VLM agents through a two-stage offline-to-online process, with a particular focus on the online phase, termed CoSo. CoSo uses soft Q-learning to improve exploration within sequential reasoning frameworks, such as chain-of-thought (CoT). The entropy term is computed by... | Rebuttal 1:
Rebuttal: We sincerely appreciate your in-depth and constructive feedback. We are also grateful for kindly pointing out that our method is well-motivated and convincing, compelling in principle, and implies high expandability and flexibility. Regarding your concerns, we provide our responses below:
> Q1: T... | Summary: This paper proposes CoSo, a reinforcement learning approach for finetuning the VLM agent. The theoretical analysis shows that CoSo can guarantee the property of convergence and performance. Experimental results demonstrate that CoSo achieves superior performance on a range of control tasks.
Claims And Evidenc... | Rebuttal 1:
Rebuttal: We sincerely appreciate your valuable comments and the time you took to review our work. We also appreciate your positive remarks about the potential applicability of our method to other challenging control tasks. Regarding your questions, we provide our responses below:
> Q1. The weighted term ... | null | null | null | null | null | null |
Probabilistic Group Mask Guided Discrete Optimization for Incremental Learning | Accept (poster) | Summary: This paper concerns the parameter-isolation methods in incremental learning. However, existing approaches often disregard parameter dependencies, resulting in an over-reliance on newly allocated parameters. To address this issue, this paper proposes Probabilistic Group Mask selection (PGM), a group-wise approa... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
**Generalization to Non-Vision tasks:** To ensure fairness and comparability, we adopt the same task setup as in [1,2], which are widely used as baselines for evaluating incremental learning performance. To further assess the generalization capability of our... | Summary: The authors propose PGM, a dependency-aware parameter-isolation framework for IL that optimizes task-specific subnetworks via probabilistic group masking. By grouping parameters and sampling masks with Gumbel-Softmax, PGM improves parameter reuse and reduces capacity overhead. Theoretical proofs link dependenc... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
**Extending to Longer Task Sequences:** To ensure fairness and comparability, we adopt the same task configurations as in [1,2] (i.e., 10, 20, and 40 tasks), which are widely recognized as baselines for evaluating incremental learning performance. To further... | Summary: This paper introduces Probabilistic Group Mask (PGM), a novel parameter-isolation method for incremental learning (IL) that addresses catastrophic forgetting by incorporating parameter dependencies during sub-network selection. PGM groups parameters, assigns probabilistic masks via Gumbel-Softmax Sampling, and... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
**Scalability to Modern Architectures:** To ensure the fairness and comparability of results, we adopt the same model architectures as in [1,2], which are widely recognized as baselines for evaluating incremental learning performance. To further assess the ge... | Summary: The paper introduces Probabilistic Group Mask (PGM) for incremental learning (IL), addressing catastrophic forgetting by modeling parameter dependencies during sub-network selection. Unlike prior methods that independently score parameters, PGM partitions parameters into groups and optimizes task-specific mask... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
**Training Overhead and Parameter Sensitivity:** While probabilistic sampling and Gumbel-Softmax reparameterization may increase implementation complexity, this design enables differentiable and learnable mask selection, which is essential for effective para... | Summary: In this paper, the author proposed probabilistic group mask selection, which aims to group parameters and explore the dependencies between them. In addition, the author used gumbel-softmax to make the sampling differentiable. To verify the method, the author conducted experiments on multiple datasets.
Claims ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments.
**Clarifying the Role of Dependency in Our Method:** Modeling parameter dependency during subnetwork selection is the key contribution of this work, enabling more effective parameter allocation in incremental learning. Specifically, this involves two key aspe... | null | null | null | null |
Benign Overfitting in Token Selection of Attention Mechanism | Accept (poster) | Summary: This paper explores benign overfitting in the token selection process of attention mechanisms, analyzing how transformers generalize despite fitting noisy training labels. It adopts feature learning framework to explain when models ignore noise (high SNR) or fit it while still generalizing well. Through theore... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We appreciate that you highly evaluate our work.
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> 1: It would be beneficial for the authors to conduct experiments with varying levels of noise in real-world datasets to further validate their findings. Additionally, a heatmap visualization on real data wo... | Summary: This paper presents theoretical analysis of benign overfitting in token selection of the attention mechanism focusing on the training dynamics and the generalization performance. The author shows that under some conditions based on signal-to-noise ratio, "benign overfitting" phenomenon occurs in the attention ... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We hope that our answer has addressed all your concerns and pray this reply will change your decision.
---
> 1: The comparison among not, benign, and harmful overfitting lacks in real-world experiments. It needs more explanation and experiments for harmful over... | Summary: The paper studies benign overfitting in token selection within the attention mechanism, using a data model consisting of signal and noise and a one-layer attention model. This work characterizes the conditions under which benign overfitting, harmful overfitting, or no overfitting occurs.
## Update After Rebut... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We hope that our answer has addressed all your concerns and pray this reply will change your decision.
---
> 1: The proof techniques lack motivation. Section 4.1 lacks sufficient explanation or intuition in the current draft.
**We have added the following impr... | Summary: The paper develops a theoretical framework to analyze the dynamics and generalization properties of token selection in attention mechanisms under label noise, focusing on a one-layer attention network for binary classification. It demonstrates that with a high signal-to-noise ratio (SNR), the model selectively... | Rebuttal 1:
Rebuttal: Thank you for your detailed review. We hope that our answer has addressed all your concerns and pray this reply will change your decision.
We first answer your question because it is an important point relating to our contribution.
---
> 1: What is the specific difficulty of label noise setting?... | null | null | null | null | null | null |
On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding | Accept (poster) | Summary: The paper study the limitation of hardmax 1-layer $r$-times looped transformer with fixed limited embedding size, extending Zhang et al. (2023) that studied looped ReLU. Furthermore, the paper suggest adopted layer-dependent scaling for looped transformer to mitigate the suggest limitation.
## update after re... | Rebuttal 1:
Rebuttal: > Q1 (W2). The theoretical results are based on constraining the hidden embedding size of transformer $(17d+9)×N$, which is very tricky. In fact, Saunshi et al. (2025) has shown that a 1-layer transformer looped can simulate the non-looped counterpart at the cost of a larger embedding size, still,... | Summary: The paper theoretically investigates the expressive power of Looped Transformers, neural architectures that recursively reuse a single Transformer block, which are appealing for their parameter efficiency and generalization capabilities. The authors define a new modulus of continuity tailored to sequence-to-se... | Rebuttal 1:
Rebuttal: > Q1 (W1). Could you elaborate on whether and how the increase in computational complexity (due to timestep encoding) affects practical deployment scenarios compared to standard Transformers?
Thank you for the insightful comment. While timestep encoding adds slight computational overhead, the inc... | Summary: This paper studies Looped Transformers from a theoretical perspective. Its first main contribution is to prove a universal approximation property, showing that a wide class of sequence functions can be represented by Looped Transformers. The main technical contribution here, compared to existing universal appr... | Rebuttal 1:
Rebuttal: > W1. Unlike a lot of other literature, the present paper follows Yun et al in only studying functions on fixed-length inputs. This restriction can be fine, but I think this should be made more transparent earlier in the paper
We respectfully point out that the study of **function approximation**... | Summary: The authors conduct theoretical analysis on the expressive power and approximation rate of looped transformers, a variant of transformers with weight tying across layers and unbounded recurrence. The analysis led to some limitations of looped transformers on function approximation, especially in memorizing inp... | Rebuttal 1:
Rebuttal: > I believe deep equilibrium models are closely related to looped transformers, but the authors don't seem to discuss them.
Thank you for the suggestion. We agree that Deep Equilibrium Models are related to Looped Transformers. We will add the following sentence to the related work section:
**l... | null | null | null | null | null | null |
Fragments to Facts: Partial-Information Fragment Inference from LLMs | Accept (poster) | Summary: The paper proposes a new privacy threat for fine-tuned LLMs called “Partial-Information Fragment Inference” (PIFI). The authors show that even if an attacker only knows a few scattered keywords about someone’s data (e.g., certain medical terms), they can still prompt the model to uncover additional, sensitive ... | Rebuttal 1:
Rebuttal: > **Classifier is considered as the baseline for experiments. Are there any other methods in the literature that you can make the comparison with? / The paper should compare its attack success rate against existing MIA or extraction attacks on the same dataset/model.**
This is an excellent questi... | Summary: This paper introduces a new threat model for extracting sensitive data from fine-tuned LLMs using only partial, unordered fragments. It proposes two data-blind attacks: a Likelihood Ratio Attack and PRISM, which refines inference using an external prior. Experiments in medical and legal domains show these atta... | Rebuttal 1:
Rebuttal: > **...assumptions about prior distributions may not always hold in real-world...**
We appreciate the comment. PRISM does make an assumption about the usefulness of a prior world model probability in adjusting the likelihood ratio (assumed to be correlated with sample membership), a premise groun... | Summary: The increasing development of large language models (LLMs) has resulted in different explorations of their trustworthiness properties. Amongst them, privacy is one of the key concerns, where prior research has shown that LLMs are prone to leaking sensitive training data through memorization and membership infe... | Rebuttal 1:
Rebuttal: > **In Sec. 4.2, how unique is the given fragment s?**
**Please see our response to Reviewer rGXA, where we give exact details on uniqueness.**
> **Why does LR-Attack and PRISM sometimes outperform classifier baseline?**
This is a great question and we’ll include this discussion in the revised ... | Summary: This paper introduces a new privacy threat model, Partial-Information Fragment Inference (PIFI), which examines how adversaries can extract sensitive information from LLMs using only small, unordered text fragments rather than full training samples. Unlike traditional memorization or membership inference attac... | Rebuttal 1:
Rebuttal: > **…discuss whether the attacks work on general-purpose LLMs**
Thank you for raising this important point. Our work specifically focuses on sensitive domains (e.g., hospitals or legal) where models are adapted on private data, and fine-tuning is often necessary because organizations cannot or do... | Summary: This paper introduces a novel Partial-Information Fragment Inference (PIFI) threat model that examines the potential for sensitive data extraction from LLMs using only unordered, publicly available fragments of information. Two data-blind attack methods are proposed: LR-Attack (likelihood ratio-based) and PRIS... | Rebuttal 1:
Rebuttal: > **Concerns about practical impacts, given the “modest” results.**
We appreciate the concern about practical impact. **While a 10% TPR at 2-5% FPR may seem modest, it can still pose a significant privacy threat (e.g., it equates to an attacker being correct 4 out of 5 times), especially scaled t... | null | null | null | null |
GLGENN: A Novel Parameter-Light Equivariant Neural Networks Architecture Based on Clifford Geometric Algebras | Accept (poster) | Summary: The paper introduces Generalized Lipschitz Group Equivariant Neural Networks (GLGENN), a neural network architecture based on Clifford geometric algebras (GAs) that is equivariant to pseudo-orthogonal transformations of a vector space with a symmetric bilinear form. GLGENN uses a weight-sharing approach for la... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are happy to answer the questions and address the concerns:
**1. Novelty of theory**
We claim that the proposed theory of generalized Lipschitz groups (GLG) in Clifford algebras (CA) $Cl_{p,q,r}$ is new (Sect. 3). These groups are introduced in... | Summary: This paper formally introduces neural networks that are equivariant with respect to generalised Lipschitz groups $\tilde{\Gamma}_{p,q,r}^{\bar{k}}$ for $k = 0, 1, 2, 3$ that are constructed from an arbitrary degenerate or non-degenerate Clifford geometric algebra (GA). The construction is based on two fundamen... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are happy to answer the questions and address the concerns as follows:
**1. Q1: Superposition in Clifford conjugation?**
Thank you. We will change the word to ‘composition’.
**2. Q2: Formatting in (10),(16),(17)?**
Thank you. We will format d... | Summary: This paper introduces a new version of Clifford Group Equivariant Neural Networks (CGENN) originally introduced by Ruhe et al., 2023 called GLGENN. The authors develop a theory for generalized Lipschitz groups which generalize and contain Clifford groups. Generalized Lipschitz groups preserve a subspace deco... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are happy to answer the questions and address the concerns as follows:
**1. Q1: Higher order features such as tensors?**
Yes, the model can accommodate higher-order features, such as tensors. For example, it can be applied to the N-body problem... | Summary: This paper introduces a novel group equivariant architecture based on Clifford geometric algebra that are equivariant to pseudo orthogonal transformation and are more parameter efficient and avoids overfitting compared to prior works in equivariant networks based on Clifford algebra. The design is based on fir... | Rebuttal 1:
Rebuttal: We thank the reviewer for the valuable feedback. We are happy to answer the questions and address the concerns as follows:
**1. Q1: Relation between generalized Lipschitz groups equivariance and parameter efficiency?**
Application of the generalized Lipschitz groups (introduced in this work) ins... | null | null | null | null | null | null |
Structured Preconditioners in Adaptive Optimization: A Unified Analysis | Accept (poster) | Summary: This paper presents a unified analysis of adaptive optimization algorithms with structured preconditioners, challenging the assumption that better approximations of full-matrix Adagrad or less structured preconditioners always yield superior performance. The authors introduce "well-structured preconditioners" ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the theoretical insight of our work. We will address your concerns below.
**A larger set of preconditioner is not necessarily better:** We appreciate the reviewer’s concern regarding our claim. Due to computational limitations, it is indeed difficult to co... | Summary: The paper studies preconditioned adaptive methods for online convex optimization in a unified manner. They define a particular class of “well-structured” preconditioners that recovers all 3 variants of the AdaGrad and the Shampoo algorithm as special instances. The main take away message of the paper is that b... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for recognizing the value of our unified framework. We will address your concern below.
**Clarification of our results:** We never claim that the regret bound for the full-matrix AdaGrad is worse than AdaGrad-norm or AdaGrad with diagonal preconditioner and we apol... | Summary: The paper provides regret bounds for a family of adaptive algorithms with structured preconditioner matrices. The analysis generalizes the technique introduced in the original Shampoo work and applies to Adagrad, Adagrad-norm, Adagrad-diag and one-sided Shampoo. Intriguingly, the paper shows that, for certain ... | Rebuttal 1:
Rebuttal: We thank the reviewer for acknowledging the value of our unified analysis. Below we will address your concerns.
**Comparison with Shampoo and Shampoo^2:** We appreciate the suggestion to include two-sided Shampoo and Shampoo^2 in the experiments. As reviewer tCyu pointed out there is lack of mo... | null | null | null | null | null | null | null | null |
KoNODE: Koopman-Driven Neural Ordinary Differential Equations with Evolving Parameters for Time Series Analysis | Accept (poster) | Summary: **Edit**: Having read through the rebuttals and the other reviews, I am satisfied with the paper and have increased my score from 3 (weak accept) to 4 (accept). I am grateful to the authors for taking the time to respond, and have left remaining specific thoughts in the Rebuttal Comment.
**Original Review**:
... | Rebuttal 1:
Rebuttal: Thank you for your deep understanding of our method and insightful suggestions.
**[1. comparison to baselines]**
We have included the baselines mentioned, Latent ODE (ODE enc) and Neural Processes, in `supplementary_table_reviewers_Snqd_waH1.pdf`, https://anonymous.4open.science/r/KoNODE-D8F2/. ... | Summary: An architecture based on neural ODE with time-varying parameters is proposed. The idea is to model the dynamics of the parameters with latent linear dynamics, which the authors motivate by referring to the Koopman operators. The superior prediction performance of the proposed method is empirically demonstrated... | Rebuttal 1:
Rebuttal: Thank you for your insightful comments.
**[1. empirical observation supports]**
We have added empirical evidence and analysis to support the two quoted claims. We will include concrete empirical observations in the revision. Please refer to [W1. lack sufficient interpretability], *Response to R... | Summary: This paper explores the challenge of modeling time series with NODEs. The authors propose a Koopman-driven framework named KoNODE that hierarchically encodes system dynamics through evolving ODE parameters and Koopman linear operators. Specifically, they introduce a three-level architecture—spanning observed s... | Rebuttal 1:
Rebuttal: Thank you for your constructive feedback.
**[W1. lack sufficient interpretability]**
We apologize for the unclear description of "revealing the fundamental driving forces of system evolution" and will clarify it in the Introduction and Experiment sections in the revision.
In our framework, the ... | Summary: This paper proposes KoNODE, a hierarchical framework that integrates Koopman operators into Neural Ordinary Differential Equations (NODEs) to learn time-evolving parameters.The authors provide theoretical error bounds for the finite-dimensional approximation of the Koopman operator and show how the proposed me... | Rebuttal 1:
Rebuttal: Thank you for your valuable feedback.
**[W1. changes in memory or runtime]**
We will move the runtime analysis from the appendix to the main text to show our advantage in convergence rate and add a discussion on memory complexity. Regarding memory, we clarify that the memory of the proposed fram... | null | null | null | null | null | null |
Structure-informed Risk Minimization for Robust Ensemble Learning | Accept (poster) | Summary: This paper introduces a novel framework to learn ensemble weights to improved out-of-distribution (OOD) robustness. The key idea is to incorporate structure relationships between training distributions to build a realistic uncertainty set. The authors proposed a computationally efficient optimization algorithm... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for their thoughtful and positive feedback. We appreciate your recognition of our paper as "very well-written and easy to follow" and that our approach is "theoretically sound while remaining computationally tractable." Your comments about the "compelling unified fr... | Summary: This paper presents SRM, a method to improve how ensemble models handle unseen data changes by leveraging the relationships between training data distributions. SRM builds a network of these distributions, measuring their similarities with a simplified distance metric. It prioritizes "central" distributions th... | Rebuttal 1:
Rebuttal: Thanks for acknowledging the novelty of our work. Below, we address the main points raised:
1. Assumption of Our Paper and Extreme OOD Scenarios
In Equation (14), we assume that the test distribution lies within a bounded divergence from a mixture of training distributions. This assumption is al... | Summary: This paper proposes a framework for learning robust ensemble weights without requiring access to test data. It aims to mitigate the over-pessimism of Distributionally Robust Optimization (DRO) by focusing the uncertainty set on more plausible structures. The idea is solid, and the proposed algorithm is computa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. We address each concern below.
**1. Comparison with [1-3] and Other OOD Generalization Methods**
While SRM and the methods in [1–3] all aim to reduce the pessimism often observed in DRO, they approach the problem from different directions:
- ... | Summary: This work proposes structure-informed risk minimization (SRM), which can be seen as a modification to the Group DRO algorithm, and applies it to robust ensemble learning.
More specifically, SRM optimizes the ensemble weight of multiple fixed pre-trained models to reduce the ensemble’s risk in the worst mixture... | Rebuttal 1:
Rebuttal: We thank the reviewer for the insightful feedback and acknowledging the novelty of our work. We appreciate the thoughtful comments and address them point-by-point below.
**1. Why Ensemble Learning? Can SRM Improve the OOD Performance of Individual Models?**
We agree that SRM is a general framewo... | null | null | null | null | null | null |
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation | Accept (poster) | Summary: This work investigates the impact of symmetrization in neural network wave functions for periodic solid systems. Specifically, the authors compare data augmentation, group averaging, and canonicalization. Contrary to other fields, the authors find that symmetrization may hurt performance, but post-hoc averagin... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback and apologize for any confusion that arise from presentation issues or differences in understanding, which we now address.
---
**Correctness of gradient.** We thank the reviewer for the typo: $\partial_\theta \partial_x^2$ should indeed be $\par... | Summary: The paper studies diagonal group symmetries in neural network solvers for many-electron Schrödinger equations, comparing different symmetrization approaches: data augmentation (DA), group averaging (GA), and post-hoc averaging (PA). The main claim is that in-training symmetrization can hurt performance while p... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback, which we address below. We will add **a new discussion and limitation section** to reflect the discussions.
---
1) **PA's effectiveness on more architectures.** Indeed it's interesting whether PA is effective beyond DeepSolid. We stress that, ... | Summary: This paper investigated different methods for incorporating diagonal symmetries in neural network solvers for the many-electron Schrödinger equation, with a particular focus on variational Monte Carlo (VMC) methods. Specifically, the authors studied three main approaches to enforce diagonal invariance: data au... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive feedback. As all reviewers suggest additional references with some overlaps and similar questions on extendability, and due to the 5000 character limit per response, we address them together below.
---
1) **Essential references.** We thank all reviewers ... | Summary: This paper investigates methods for incorporating diagonal group symmetries into neural network wave function ansatze for solving the many-electron Schrödinger equation via Variational Monte Carlo (VMC). The authors compare three main approaches: data augmentation (DA), group averaging (GA) and canonicalizatio... | Rebuttal 1:
Rebuttal: We thank the reviewer for the positive and helpful feedback. We address the questions below.
---
1) **Relevance to other areas of physics simulation with similar symmetry constraints arise.** We agree that this would add value and is a very interesting avenue of future work. **In the revision, we... | null | null | null | null | null | null |
Feasible Action Search for Bandit Linear Programs via Thompson Sampling | Accept (poster) | Summary: This paper studies the linear feasible action search problem, where the goal is to find a point in the input space such that some linear constraints are satisfied. In each round the learner can query a point and observe a noisy version of its constraints. The authors provide a Thompson sampling approach to thi... | Rebuttal 1:
Rebuttal: Our thanks for your work.
Comparison to Gangrade et al 2024b. Thanks for pointing out these omissions, which we will fix.
- Theoretical: Gangrade et al 2024b only study testing. We can extend it to FAS via something like our Lemma 7. In this case, the bound is a) $O(d^2/\varepsilon^2 M_*^2)$ for ... | Summary: This paper studies the problem of identifying a point that satisifies a set of linear constraints, using only noisy observations of the linear constraints.
They give an algorithm that identifies a strictly feasible point after $\tilde{O}(\frac{d^3}{\epsilon^2 M^2})$ rounds, where $M_*$ is the largest safety ma... | Rebuttal 1:
Rebuttal: Our thanks for your work.
Questions:
- Corollary 10: This is indeed an important point, thanks for your careful reading. The issue is resolved by Remark 11 (page 8) and Appendix F.1 (page 47) of the paper of Pacchiano et al. (https://arxiv.org/pdf/2401.08016).
- In short, remark 11 summaris... | Summary: This work studies Feasible Action Search problems with linear constraints, in which a learner aims to find a point with maximal safety margin. The learner may declare that the constraints are infeasible if it fails to find a feasible point.
The authors suggest an algorithm called FAST. It is based on Thompson... | Rebuttal 1:
Rebuttal: Our thanks for your work.
- Max-safety-margin v/s only feasible. Thanks for bringing up this important point. The short answer is that our setup already accomodates the viewpoint you express through our use of the multiplicative approximation.
- Indeed, any feasible point would have a safety ... | Summary: The paper studies the feasible action search problem for linear bandits. In particular, the goal of the learner is to identify a point in a convex set that satisfies a set of linear constraints. The learner repeatedly interacts with the environment and receives as feedback the value of all the constraints for ... | Rebuttal 1:
Rebuttal: Our thanks for your work. Thanks also for the interesting reference. The section 8 of this paper certainly describes a related idea, although there is no real adaptation to the value of the Slater parameter ($\equiv$ margin), which is an important aspect of our study. We will both try to carefully... | null | null | null | null | null | null |
Towards Robustness and Explainability of Automatic Algorithm Selection | Accept (spotlight poster) | Summary: The paper focuses on the automatic algorithm selection problem. It aims to improve the explainability and robustness of algorithm selection. Main contributions include:
(1) The most significant innovation of this paper is that it changes the modeling approach of the algorithm selection task. Traditionally, mo... | Rebuttal 1:
Rebuttal: > Weakness 1 / Q3
Thanks for your insightful comments. In practical scenarios, informative algorithm features are not always readily available, just as pointed out by Reviewer nswt. In this study, we recognize that the availability of algorithm features has a vital impact on the model's performan... | Summary: The paper introduces a new approach to algorithm selection using directed acyclic graphs (DAGs) and causal relations. The approach focuses on modeling the causal relations between problem features and algorithm features. The authors argue that this method not only improves the accuracy of algorithm selection b... | Rebuttal 1:
Rebuttal: > Experiments: Missing SOTA approaches. None of the baselines use algorithm features.
Thanks for your valuable suggestions. We acknowledge that our experiments lacked some SOTA comparison methods. In response, we have now incorporated ASAP and AutoFolio into our experiments.
Regarding the absenc... | Summary: The paper argues that current approaches to automatic algorithm selection are mainly based on the correlation between algorithm performance and problem meta-features, which are susceptible to data bias and distributional variations, and lack robustness. The thesis proposes to use DAG to represent the causal re... | Rebuttal 1:
Rebuttal: > Weakness 1
Thanks for your valuable comments. It's right that "for a defined problem characteristic, the optimal algorithm should also be defined", which implies that $P(AF|PF)$ remains constant. In Appendix E.4, our intention was not to change $P(AF|PF)$, but rather to manipulate the marginal ... | Summary: The paper "Towards Robustness and Explainability of Automatic Algorithm Selection" introduces a novel approach to algorithm selection using a directed acyclic graph (DAG) to model the causal relationships between problem features and algorithm features. The proposed method, DAG-based Algorithm Selection (DAG-A... | Rebuttal 1:
Rebuttal: > The paper's performance on GLUHACK-18 and SAT03-16-INDU, is not as strong as on others, indicating potential limitations ...
We acknowledge that DAG-AS did not perform well on GLUHACK-18 and SAT03-16-INDU. However, it is unjustified to undermine the significance of DAG-AS merely based on its pe... | null | null | null | null | null | null |
Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection | Accept (spotlight poster) | Summary: This paper proposes the Adaptive Multi-prompt Contrastive Network for few-shot out-of-distribution detection, aiming to improve OOD detection performance when only limited labeled in-distribution samples are available. The method introduces adaptive prompts to learn class distributions and enhance the separati... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer oFtr for the valuable comments and provide the following detailed responses to all weaknesses.
---
### **W1: Figure 1 is not referenced or discussed in the main text. The caption of Figure 7 contains a error: "right" and "left" are mistakenly reversed.**
Thanks a lot... | Summary: Out-of-distribution (OOD) detection is an important machine learning task. Few-shot OOD detection is an important yet challenging setting in OOD detection task, where only a few labeled ID samples are available. This paper proposes a novel and clear few-shot OOD detection model that considers an interesting an... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 8TNR for the valuable comments and provide the following detailed responses to all weaknesses.
---
### **W1: A little typo in Figure 1 caption. “(c) Brief framework of our method.” should be “(d) Brief framework of our method.”**
Thanks a lot for your valuable sugges... | Summary: This work introduces a new method for few-shot out-of-distribution (OOD) detection. Unlike previous approaches that largely overlook the diverse characteristics among different classes, the proposed method constructs both in-distribution (ID) and OOD prompts and designs multiple contrastive losses to learn a b... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer 1bUn for the valuable comments and provide the following detailed responses to all weaknesses.
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### **W1: Prompt learning is widely explored in the community. The idea of using learnable prompts for both ID and OOD classes is straightforward. However, the design of... | Summary: This paper proposes to address the novel and challenging task, multi-diversity few-shot OOD detection. Unlike the previous methods that ignore the distinct diversity between different classes in the few-shot OOD detection task, this paper presents a novel network AMCN. The proposed method first transposes ID p... | Rebuttal 1:
Rebuttal: We sincerely thank Reviewer J7Ff for the valuable comments and provide the following detailed responses to all weaknesses.
---
### **W1: Authors should move the definition of c to the third paragraph of Section 3.2.**
Thanks a lot for your valuable suggestion. We will revise it in our revised v... | null | null | null | null | null | null |
Policy Regularization on Globally Accessible States in Cross-Dynamics Reinforcement Learning | Accept (spotlight poster) | Summary: This paper proposes Accessible State Oriented Policy Regularization (ASOR), a reward shaping technique for offline and online, off-policy RL with dynamic shift. The work is inspired by the dynamic-agnostic state distribution matching in Imitation from Observation (IfO), but points out that naively imitating e... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and insightful comments. Responses are provided as follows. The linked file is available [here](https://anonymous.4open.science/api/repo/ICML_ASOR_Rebuttal-01CB/file/rebuttal_append_file.pdf).
**Q1: Possibly empty accessible state set**
A: Tasks with no... | Summary: The paper addresses the challenge of learning policies when the environment dynamics vary such that expert state trajectories may not always be accessible under dynamics shifts. To overcome this, the authors present the Globally Accessible States with a formal definition and the F-Distance, a measure of the d... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and insightful comments. Responses are provided as follows. The linked file is available [here](https://anonymous.4open.science/api/repo/ICML_ASOR_Rebuttal-01CB/file/rebuttal_append_file.pdf).
**Q1: Robustness to hyperparams**
A: Thanks for mentioning t... | Summary: The paper pinpoints a flaw in existing IfO methods where state inaccessibility due to changing environment dynamics can disrupt the similarity of expert state distributions. To tackle this, it presents a policy regularization approach centered on globally accessible states. The proposed framework combines rewa... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and insightful comments. Responses are provided as follows.
**Q1: Determining accessibility in practice**
A: Tasks with potentially empty globally accessible state set are indeed extreme cases where ASOR may degrade into the base RL algorithm. An exampl... | Summary: This paper studies policy learning in cases where the environment dynamics may vary during training. Even when expert demonstrations or replay buffers are provided, some states within them may be inaccessible. In such situations, the policy should identify which states are globally accessible and consider only... | Rebuttal 1:
Rebuttal: We thank the reviewer for the constructive and insightful comments. Responses are provided as follows. The linked file is available [here](https://anonymous.4open.science/api/repo/ICML_ASOR_Rebuttal-01CB/file/rebuttal_append_file.pdf).
**Q1: Practical estimation concerns**
A: We discuss in Sec. ... | null | null | null | null | null | null |
On the Power of Context-Enhanced Learning in LLMs | Accept (spotlight poster) | Summary: The paper introduces *context-enhanced learning* as a training setup where an LLM is fine-tuned on a task with extra context provided, but no gradient is directly taken on that context.
The main claims of the paper are:
1. Context-enhanced learning improves training sample efficiency on reasoning tasks. Thi... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive comments and questions!
Our responses to your questions and concerns are as follows:
---
### Applying Context-Enhanced Learning to Real World Data
Context-enhanced learning (CEL) has been shown to outperform standard training or fine-tuning approaches th... | Summary: This paper introduces a new concept called context-enhanced learning for LLMs, where they add context related to the task and training time step t in addition to the training data of x and y, and no loss is applied to the context text. To study how this will help LLM learning, they introduced a task called mul... | Rebuttal 1:
Rebuttal: Thank you so much for your constructive comments (especially regarding section 3.3) and questions!
Our responses to your questions and concerns are as follows:
---
### Definition of Necessary Rules and Unused Rules (line 165)
For an input sequence $s_1$ of length 20-40 tokens, each translation... | Summary: This paper investigates the power of context-enhanced learning in large language models. The authors propose a synthetic machine translation task that utilizes phrasebooks to transform initial strings. Experimentally, the paper demonstrates that if the base model is MLT(d, n)-ICL-capable, context-enhanced lear... | Rebuttal 1:
Rebuttal: Thank you so much for your comments and insightful questions!
Our responses to your questions are as follows:
---
### What accounts for the OOD generalization to empty context?
**Short Answer**: We believe this OOD generalization is fundamentally a form of compositional generalization, which is... | Summary: The authors study the impact of augmenting context with additional data for learning in LLMs. Notably, on the added data no autoregressive gradients are computed. The authors consider a stylized problem of multi-layer text translation over finite alphabet where text from one language is translated to another i... | Rebuttal 1:
Rebuttal: Thank you so much for your comments and insightful questions!
Please find our responses to your questions / concerns below.
---
### Generalization in Context-Enhanced Learning
>"The MLT task does not shed light into the generalization capability of the technique ... the setup only shows quicke... | null | null | null | null | null | null |
Density Ratio Estimation with Conditional Probability Paths | Accept (poster) | Summary: This paper introduces a new method for density ratio estimation called conditional time score matching (CTSM). CTSM estimates the time score along a probability path connecting two densities instead of directly estimating the ratio of two densities. By conditioning on additional variables, the authors propose ... | Rebuttal 1:
Rebuttal: Thank you for the very positive assessment. Below we respond to the comment on the MNIST experiment.
We indeed showed results only for pre-trained Gaussian NF, since it was the most computationally stable. We have some experimental results also for Copula and RQ-NSF utilizing essentially the same... | Summary: The authors tackle the problem of estimating the ratio of two probability densities, improving upon speed and accuracy of prior work. They also establish a theorem on a guarantee of the error in the estimated density ratio. The method applies the "marginalization trick" (a la flow matching) to make the learnin... | Rebuttal 1:
Rebuttal: Thank you for your positive assessment. We agree that our learning objective (CTSM) is similar to the learning objective in flow matching (Eq 5, [1]). However, our method is not a straightforward application of flow matching given that, fundamentally, we learn a different quantity. Flow matching l... | Summary: This paper proposes the conditional time score matching (CTSM) objective, which is a variant of the time score matching (TSM) objective proposed in the recent density ratio estimation literature.
The CTSM objective provides a principled way to detour the computational drawback of TSM, which requires two automa... | Rebuttal 1:
Rebuttal: Thank you for the very positive assessment and for spotting the typo in Eq. 45 which is now corrected. Regarding our theoretical guarantees, we chose the KL divergence out of convenience for the derivations in Appendix D.3. A similar analysis could indeed be used to bound the expected value of the... | null | null | null | null | null | null | null | null |
AuPair: Golden Example Pairs for Code Repair | Accept (poster) | Summary: The paper introduces Aupair, which is a customed method that generates golden example pairs for enhancing code repair performance. In their work, each pair contains an initial guess and its fixes, which are used as in-context example at inference time to generate a repaired solution. During inference call, the... | Rebuttal 1:
Rebuttal: 1. discuss related work that uses adversarial approaches for reference.
We will include references to adversarial approaches such as [1,2] in the Related Work section.
2. Only unit test scores as the metric to evaluate correctness of code repairs
In this work, we have used unit test scores for ... | Summary: The paper introduces AuPair, a novel algorithm designed to improve Large Language Models' (LLMs) performance on code repair tasks through inference-time computation. AuPair leverages in-context learning by synthesizing an ordered set of example pairs (called "AuPairs") consisting of initially incorrect code ("... | Rebuttal 1:
Rebuttal: 1. Limited exploration of failure cases
While AuPairs have been shown to significantly boost performance, they can occasionally have unintended impacts as well. The following table contains the percentage of CodeForces problems in which some fixes had a decrease in fix score compared to the initi... | Summary: Paper introduces AuPair, an inference time algorithm to improve code repair capabilities of LLM. The core idea lies in first a diverse of generating golden pairs (guess, fix) using an LLM and then using a submodular selection algorithm to identify and generate an ordered set of golden example pairs. During inf... | Rebuttal 1:
Rebuttal: 1. Including a more diverse set of models would strengthen evaluation
We have included 5 models spanning 3 model families across 7 datasets in the existing results – Gemini, GPT, Gemma; the results clearly indicate that AuPair works across models. We understand the reviewer's concern and agree th... | null | null | null | null | null | null | null | null |
Deep Streaming View Clustering | Accept (poster) | Summary: This paper proposes a deep streaming view clustering algorithm (DSVC). It considers the scenario where data is acquired in the form of view streams in clustering tasks. DSVC aligns the prototype knowledge of the current view with the historical knowledge distribution, thereby mitigating the concept drift issue... | Rebuttal 1:
Rebuttal: Thank you for taking the time to review our submission and provide valuable feedback.
**W1: Ablation study without the cross-attention mechanism**
**AW1:** To validate the effectiveness of the cross-attention mechanism, we conducted an ablation study. As shown in the following table, incorporat... | Summary: This paper identifies the concept drift problem in streaming view clustering, which causes outdated models to fail to adapt to new view data. To address this, the authors employ knowledge aggregation learning to simultaneously reconstruct prototype knowledge from the features and reconstruct features from the ... | Rebuttal 1:
Rebuttal: **W1: The cross-attention mechanism appears to have been used in other works as well. Could the authors clarify the novelty of its application in this context?**
**AW1:** Other methods, such as ProImp [1], are designed for static multi-view clustering tasks, where the attention mechanism primaril... | Summary: The paper presents Deep Streaming View Clustering (DSVC), a method designed to tackle concept drift in multi-view clustering with streaming data. DSVC features three modules: Knowledge Aggregation Learning (KAL) for feature extraction, Distribution Consistency Learning (DCL) to align current and historical kno... | Rebuttal 1:
Rebuttal: We sincerely appreciate the time you have taken to review our submission and provide valuable feedback.
**Q1: Claims And Evidence**
**Q1(a): Figure 1(a) needs more detailed explanation about the model and evaluation settings.**
**AQ1(a):** Figure 1 (a) illustrates that due to distribution imb... | Summary: This paper explores a rarely addressed area in multi-view clustering, namely, streaming view clustering. In this work, the authors utilize the Knowledge Aggregation Learning (KAL) module to extract features and prototype knowledge. Subsequently, the Distribution Consistency Learning (DCL) module is employed to... | Rebuttal 1:
Rebuttal: Firstly, we sincerely thank you for your detailed review and constructive feedback, which have greatly contributed to improving the presentation of our submission.
**S1: For instance, in Eq. 4, what does the symbol d represent?**
**AS1:** Thank you very much for your valuable suggestions. In Eq.... | null | null | null | null | null | null |
TimeBase: The Power of Minimalism in Efficient Long-term Time Series Forecasting | Accept (spotlight poster) | Summary: This article presents a lightweight model requiring only 0.39k parameters, providing an extremely effective method for time-series forecasting. Extensive experiments have been conducted to verify the effectiveness of such a proposal.
Claims And Evidence: The claims made in the submission are supported by suff... | Rebuttal 1:
Rebuttal: Dear Reviewer Pc2P,
Thank you for taking the time to review our work and for your valuable feedback.
---
>**D1. Discussion on Other Related Works**
Thank you and `Reviewer 2K2o` for suggesting a comparison with related work and clarifying our distinctions and contributions. We provide an over... | Summary: This submission focuses on designing a simple yet efficient model to tackle complex forecasting problems and presents TimeBase, an ultra-lightweight network for long-term time series forecasting (LTSF). The experiments show that TimeBase has a small number of parameters, low computation and memory usage, and e... | Rebuttal 1:
Rebuttal: Dear Reviewer JfQc,
Thank you for your interest in our paper and the constructive feedback you provide. We will address your questions point by point.
---
>**W1. Real-world low-resource scenarios**
Thank you for your suggestion! TimeBase can be applied in various resource-constrained enviro... | Summary: This paper proposes an ultra-lightweight framework for long-term time series forecasting, TimeBase, which segments the time series, extracts basis components, and then performs forecasting. Furthermore, TimeBase can serve as a plug-and-play module to reduce the complexity of other patch-based models.
Claims A... | Rebuttal 1:
Rebuttal: Dear Reviewer 2K2o,
We greatly appreciate your constructive feedback and have made our best to address your concerns.
---
>**C1. Sentence Explanation**
|Term| Explain|
|-|-|
|Essential Patterns|The compact set of basis components extracted from redundant segments|
|Interactions|Inter-corr... | Summary: This manuscript focuses on improving efficiency in long-term time series forecasting. The proposed framework comprises only two linear layers, yet it achieves superior efficiency compared to recent state-of-the-art methods. Besides, it provides extensive theoretical analysis and plenty of experiments to demons... | Rebuttal 1:
Rebuttal: Dear Reviewer n697,
Thank you for taking the time to review our paper and for your valuable suggestions to improve its quality.
---
>**W1. Basis Orthogonal Restriction Analysis:**
We appreciate your suggestion. From the perspective of the data space, the orthogonality of the basis vectors enh... | null | null | null | null | null | null |
Efficient Quantification of Multimodal Interaction at Sample Level | Accept (poster) | Summary: In this paper, the authors presented a novel method for efficient quantification of multimodal interaction for a single multimodal sample. Pointwise mutual information may be negative, so monotonicity over the existing redundancy framework does not hold for single samples; therefore, the authors proposed an al... | Rebuttal 1:
Rebuttal: We sincerely thank the reviewer for the positive feedback and constructive comments on our work. We have carefully addressed the citation issue mentioned in the review, and examine other reference and formualtions carefully. Thank you again for your time and effort.
---
Rebuttal Comment 1.1:
Com... | Summary: The paper proposes a novel framework to resolve conflicts between pointwise mutual information (PMI) and Partial Information Decomposition (PID) axioms in multi-modal learning. Traditional PID axioms (non-negativity, monotonicity) are defined for distribution-level redundancy but conflict with sample-level PMI... | Rebuttal 1:
Rebuttal: Thank you for their valuable suggestions. Here are our responses to the questions.
**Q1: Visualization of $r^+$ and $r^-$**
A1: Thank you for this valuable suggestion. To demonstrate the function of the $r^+$ and $r^-$ components derived from the total redundancy $r$, we conducted experiments... | Summary: The paper tackles an important question of capturing interactions between modalities and proposes a lightweight
entropy-based multimodal interaction estimation approach for efficient and precise sample-wise interaction measurement across various continuous distributions. The authors demonstrate the efficacy o... | Rebuttal 1:
Rebuttal: We thank the reviewers for their valuable suggestions. Here are our responses to the questions raised about our paper.
**Q1: The contribution of this work compared to the work from Morency's Lab.**
A1: Thank you for this question. The core contribution of our work, compared to foundational studi... | Summary: The paper presents LMSI, a light-weight framework for estimating redundant, unique and synergistic information in multimodal tasks through sample-level partial information decomposition. While the previous approaches require computationally expensive tasks like distribution estimation, LMSI is cheaper without ... | Rebuttal 1:
Rebuttal: Thank you for your valuable comments and suggestions. Here are our responses:
**Q1: Model biases in dataset interactions**
A1: We appreciate the reviewer's valuable point. Indeed, quantifying multimodal interactions requires models, making the estimates inherently model-dependent. Consistent wit... | null | null | null | null | null | null |
Upcycling Text-to-Image Diffusion Models for Multi-Task Capabilities | Accept (poster) | Summary: This paper proposes Multi-Task Upcycling (MTU), a method to extend pre-trained text-to-image diffusion models for multiple image-to-image generation tasks without significantly increasing computational complexity or model parameters. The key idea is to replace the Feed-Forward Network (FFN) layers with smaller... | Rebuttal 1:
Rebuttal: We thank you for your insightful feedback. We answer your concerns below.
**Concerns over scalability of experts in SDXL:** We acknowledge your concern regarding the performance drop observed when increasing the number of experts in SDXL —a point also raised by Reviewer ptLD. In our rebuttal for... | Summary: This paper suggests that upcycling the text-to-image (T2I) diffusion model when adapting it on the multi-task learning. In detail, when fine-tuning T2I diffusion mode with multiple tasks (e.g. SR, Inpainting, T2I generation, Image editing), they argue to utilize Mixture-of-Experts (MoE). For the motivation, th... | Rebuttal 1:
Rebuttal: Thank you for your insightful review. We acknowledge that your concerns mainly pertain to Table 4, where we present an ablation study on the number of experts for both SDv1.5 and SDXL. We understand the reviewer is concerned regarding the significance of our method, given that in many cases a sing... | Summary: The paper aims to achieve multi-tasking ability for a pre-trained T2I model, via a "lightweight" approach - MTU.
The paper starts from the insight that Feed-Forward Networks (FFNs) receive the most significant change when finetuned to a new task for a pre-trained T2I model.
MTU splits original FFNs into small... | Rebuttal 1:
Rebuttal: Thank you for your positive review, and for recognizing that MTU is the only approach so far to handle 4 different downstream tasks. We address your concerns below.
1. **Quality of image generation:** We believe our MTU model produces images of comparable quality to the base model. While Figure ... | Summary: The paper proposes Multi-Task Upcycling (MTU), an extension of pretrained text-to-image models for multi-task on-device deployment. The authors first investigate the differences between fine-tuned weights and pretrained initial weights across different layers in LDM. Based on this observation, they split the F... | Rebuttal 1:
Rebuttal: Thank you for your review. We have answered your questions below.
**Weight distributions of FFNs in SD3.5 for image-to-image tasks:** Thank you for your comment. To the best of our knowledge, general image-to-image generation using MMDiT-based models has not been thoroughly explored. Although th... | null | null | null | null | null | null |
AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling | Accept (poster) | Summary: This paper explores the potential of LLMs to elicit prior distributions, aiming to reduce sample complexity in Bayesian inference, particularly in data-scarce healthcare domains. The authors compare two LLM-based prior elicitation methods: their proposed AutoElicit and In-Context Prior. AutoElicit directly pro... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their time and comments to improve our paper. We are pleased that they felt that this was commendable work that is well-written, easy to follow, and highly relevant for healthcare. We are glad that the methods were considered straight-forward, but effective,... | Summary: This work proposes a framework to elicit prior distributions over parameters from LLMs, and use these in connection with linear models, with the goal to achieve transparent and interpretable, yet performant models.
Claims And Evidence: The paper is well written and relatively easy to follow.
Methods And Eval... | Rebuttal 1:
Rebuttal: We would like to thank the reviewers for their feedback and time. We are happy that the work is considered well-written, with strong experiments, clear and informative figures, and a detailed appendix. We are pleased that the evaluation on a private dataset is appreciated and provides strong motiv... | Summary: Linear predictive models remain valuable for applied researchers. However, principled Bayesian approaches to such models require the specification of some prior. What prior should researchers use for different parameters? Good priors may be informed by expert information, but such expert knowledge can be hard ... | Rebuttal 1:
Rebuttal: We appreciate the reviewer's kind words and time. We are pleased that our work was considered well-written with thorough experiments and appendices. We are happy that the extensive literature review, discussion of elicitation costs, and LLM memorisation experiments were appreciated.
In our rebutt... | Summary: The paper considers a prior elicitation method based on query large language models, rather than human experts, for the purposes of fitting Bayesian linear models. They make a distinction between the explicitly elicited priors supplied by their method, and the implicit priors used by the LLM when doing in-cont... | Rebuttal 1:
Rebuttal: We would like to thank the reviewer for their comments and constructive feedback to improve our work. We are happy the work was found to be well-written, easy to follow, and well motivated. We are pleased that the use of strong experiments, evaluation of LLM memorisation, and use of our privately ... | null | null | null | null | null | null |
Model-Based Exploration in Monitored Markov Decision Processes | Accept (poster) | Summary: This paper considers model-based reinforcement learning under partially observable rewards. In classical reinforcement learning, the agent always receives a reward upon executing an action in the current state of the underlying MDP. However, in practical scenarios, such as when a human provides intermittent re... | Rebuttal 1:
Rebuttal: We thank Reviewer TsqN for providing comprehensive and in-depth feedback! We are glad you found the paper well-written and easy to follow. Here, we try to address the points mentioned in order:
> I’m wondering to what extent this assumption of full observability restricts potential applications... | Summary: The current paper consider the task of bounding the sample complexity of exploration in monitored MDP where the reward of the environment is observed only for some of the state action pairs and where the minimum non zero probability of observing an environment reward is lower bounded by $\rho$.
Interesting th... | Rebuttal 1:
Rebuttal: We thank Reviewer gjFL for providing valuable feedback! We are glad you found the algorithmic derivation very well explained. Here, we try to address the points mentioned in order:
> An additional theoretical result that the author could try to obtain is the regret from the initial distribution (... | Summary: This work extends model-based interval estimation with exploration bonus (MBIE-EB), a well-known model-based exploration algorithm for MDPs with PAC guarantees, to the monitored MDP (Mon-MDP).
The monitored MDP relaxes the assumption that the reward is observed at every time step and, instead, let this be det... | Rebuttal 1:
Rebuttal: We thank Reviewer fk7j for providing valuable feedback! Here, we try to address the points mentioned in order:
> It would have been nice to see some non Mon-MDP algorithms performance, especially on MDPs that are less obviously designed with these Mon-MDP algorithms in mind. Similarly, some ablat... | Summary: In this paper, authors develop a model-based interval estimation algorithm for Monitored MDPs. Authors prove typical desired properties about this algorithm, and compare its performance on a suite of 24 benchmark environments to the previous SOTA for Mon-MDPs called $E^2$. The results show notable improvements... | Rebuttal 1:
Rebuttal: We thank Reviewer VJG2 for providing valuable feedback! Here, we try to address the points mentioned in order:
> Appendix B.4 states that with random initialization, essentially no learning occurs, but I think a Figure showing this and comparing the performance to MBIE-EB would be useful.
Figure... | null | null | null | null | null | null |
Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models | Accept (poster) | Summary: This work proposes a method to speed up diffusion models by skipping steps (Dash) and by compensating errors for the Dash (Dot). The Dash model uses a pre-trained model with mere skipping steps (i.e., existing diffusion models such as Stable Diffusion, but using larger, less steps) while the Dot model must be ... | Rebuttal 1:
Rebuttal: Thanks a lot for your detailed comments and the recognition of our method.
**1. To your main concern** about comparisons of Morse with recent related methods, **our responses include 4 parts**:
**Part 1: Comparison with DeepCache and PFDiff.** In formulation, they and our Morse explore the tempo... | Summary: This paper proposes a new faster sampling method for diffusion models. The goal of the paper is to provide a faster sampling method, not sacrificing performance (unlike many recent distillation-based methods). The main idea is to use another (the "dot") model in the sampling process. This additional sampler "c... | Rebuttal 1:
Rebuttal: Thank you so much for the constructive comments, and the recognition of our work including the proposed method, the experiments and the performance. Please see our below responses to your concerns and questions one by one.
**1 To your concern about** “However, one complaint I have is that there i... | Summary: The paper introduces Morse, a framework for accelerating diffusion model sampling by training a lightweight model (Dot) to emulate a slower pretrained diffusion model (Dash). Dot leverages additional inputs, such as sampling history from earlier timesteps, to improve efficiency. Its architecture mirrors the or... | Rebuttal 1:
Rebuttal: Thank you for recognizing our work and constructive comments.
**1. To your main concern** about the comparison of Morse with existing methods for accelerating diffusion inference, **our responses include 3 parts**:
**Part 1: Comparison with Feature Reuse.** Following the suggestion by you and Re... | null | null | null | null | null | null | null | null |
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