GenAI4ELab/papercli-papers huggingface dataset:
Equivariant Polynomial Functional Networks
Authors: Thieu Vo; Hoang V. Tran; Tho Tran Huu; An Nguyen The; Thanh Tran; Minh-Khoi Nguyen-Nhat; Duy-Tung Pham; Tan Minh Nguyen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: A neural functional network (NFN) is a specialized type of neural network designed to process and learn from entire neural networks as input data.  Recent NFNs have been proposed with permutation and scaling equivariance based on either graph-based message-passing mechanisms or parameter-sharing mechanisms. Compared to graph-based models, parameter-sharing-based NFNs built upon equivariant linear layers exhibit lower memory consumption and faster running time. However, their expressivity is limited due to the large size of the symmetric group of the input neural networks. The challenge of designing a permutation and scaling equivariant NFN that maintains low memory consumption and running time while preserving expressivity remains unresolved. In this paper, we propose a novel solution with the development of MAGEP-NFN (**M**onomial m**A**trix **G**roup **E**quivariant **P**olynomial **NFN**). Our approach follows the parameter-sharing mechanism but differs from previous works by constructing a nonlinear equivariant layer represented as a polynomial in the input weights. This polynomial formulation enables us to incorporate additional relationships between weights from different input hidden layers, enhancing the model's expressivity while keeping memory consumption and running time low, thereby addressing the aforementioned challenge. We provide empirical evidence demonstrating that MAGEP-NFN achieves competitive performance and efficiency compared to existing baselines.
PIGDreamer: Privileged Information Guided World Models for Safe Partially Observable Reinforcement Learning
Authors: Dongchi Huang; Jiaqi WANG; Yang Li; Chunhe Xia; Tianle Zhang; Kaige Zhang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Partial observability presents a significant challenge for Safe Reinforcement Learning (Safe RL), as it impedes the identification of potential risks and rewards. Leveraging specific types of privileged information during training to mitigate the effects of partial observability has yielded notable empirical successes. In this paper, we propose Asymmetric Constrained Partially Observable Markov Decision Processes (ACPOMDPs) to theoretically examine the advantages of incorporating privileged information in Safe RL. Building upon ACPOMDPs, we propose the Privileged Information Guided Dreamer (\textit{PIGDreamer}), a model-based RL approach that leverages privileged information to enhance the agent's safety and performance through privileged representation alignment and an asymmetric actor-critic structure.
Our empirical results demonstrate that \textit{PIGDreamer} significantly outperforms existing Safe RL methods. Furthermore, compared to alternative privileged RL methods, our approach exhibits enhanced performance, robustness, and efficiency.
Codes are available at: https://github.com/hggforget/PIGDreamer.
Heads up! Large Language Models Can Perform Tasks Without Your Instruction via Selective Attention Head Masking
Authors: Senyu Han; Hongchuan Zeng; Kai Yu; Lu Chen
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large language models (LLMs) consist of numerous Transformer modules, and while the models can perform various functions, it remains an open question of how these modules are combined to elicit distinct inherent functionalities. In this paper, we investigate the modules inside LLMs and demonstrate that, by simply masking or retaining specific attention heads during inference, LLMs can exhibit specific task functionalities without requiring explicit instructions or modifications to the model parameters. Experiments across various models and tasks reveal that LLMs inherently encode ``functional pathways'', the structured groups of interdependent attention heads that are crucial for executing specific tasks. These pathways not only govern the model's functional behaviors but also enhance parameter efficiency, as suppressing attention heads outside the pathway can improve task performance. The code is available in this repository: [https://github.com/OpenDFM/HeadsUp](https://github.com/OpenDFM/HeadsUp).
No-Regret is not enough! Bandits with General Constraints through Adaptive Regret Minimization
Authors: Martino Bernasconi; Matteo Castiglioni; Andrea Celli
Venue: ICML 2025 (ICML 2025 poster)

Abstract: In the bandits with knapsacks framework (BwK) the learner has $m$ resource-consumption (i.e., packing) constraints. We focus on the generalization of BwK in which the learner has a set of general long-term constraints. The goal of the learner is to maximize their cumulative reward, while at the same time achieving small cumulative constraints violations. In this scenario, there exist simple instances where conventional methods for BwK fail to yield sublinear violations of constraints. We show that it is possible to circumvent this issue by requiring the primal and dual algorithm to be weakly adaptive. Indeed, even without any information on the Slater's parameter $\rho$ characterizing the problem, the interaction between weakly adaptive primal and dual regret minimizers leads to a ``self-bounding'' behavior of dual variables. In particular, their norm remains suitably upper bounded across the entire time horizon even without explicit projection steps. By exploiting this property, we provide best-of-both-worlds guarantees for stochastic and adversarial inputs. In the first case, we show that the algorithm guarantees sublinear regret. In the latter case, we establish a tight competitive ratio of $\rho/(1+\rho)$. In both settings, constraints violations are guaranteed to be sublinear in time. Finally, this results allow us to obtain new result for the problem of contextual bandits with linear constraints, providing the first no-$\alpha$-regret guarantees for adversarial contexts.
Invariant Deep Uplift Modeling for Incentive Assignment in Online Marketing via Probability of Necessity and Sufficiency
Authors: Zexu Sun; Qiyu Han; Hao Yang; Anpeng Wu; Minqin Zhu; Dugang Liu; Chen Ma; Yunpeng Weng; Xing Tang; xiuqiang He
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: In online platforms, incentives (\textit{e.g}., discounts, coupons) are used to boost user engagement and revenue. Uplift modeling methods are developed to estimate user responses from observational data, often incorporating distribution balancing to address selection bias. However, these methods are limited by in-distribution testing data, which mirrors the training data distribution. In reality, user features change continuously due to time, geography, and other factors, especially on complex online marketing platforms. Thus, effective uplift modeling method for out-of-distribution data is crucial. To address this, we propose a novel uplift modeling method \textbf{I}nvariant \textbf{D}eep \textbf{U}plift \textbf{M}odeling, namely \textbf{IDUM}, which uses invariant learning to enhance out-of-distribution generalization by identifying causal factors that remain consistent across domains. IDUM further refines these features into necessary and sufficient factors and employs a masking component to reduce computational costs by selecting the most informative invariant features. A balancing discrepancy component is also introduced to mitigate selection bias in observational data. We conduct extensive experiments on public and real-world datasets to demonstrate IDUM's effectiveness in both in-distribution and out-of-distribution scenarios in online marketing. Furthermore, we also provide theoretical analysis and related proofs to support our IDUM's generalizability.
Learning from True-False Labels via Multi-modal Prompt Retrieving
Authors: Zhongnian Li; Jinghao Xu; Peng Ying; Meng Wei; Xinzheng Xu
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Pre-trained **V**ision-**L**anguage **M**odels (VLMs) exhibit strong zero-shot classification abilities, demonstrating great potential for generating weakly supervised labels. Unfortunately, existing weakly supervised learning methods are short of ability in generating accurate labels via VLMs. In this paper, we propose a novel weakly supervised labeling setting, namely **T**rue-**F**alse **L**abels (TFLs) which can achieve high accuracy when generated by VLMs. The TFL indicates whether an instance belongs to the label, which is randomly and uniformly sampled from the candidate label set. Specifically, we theoretically derive a risk-consistent estimator to explore and utilize the conditional probability distribution information of TFLs. Besides, we propose a convolutional-based **M**ulti-modal **P**rompt **R**etrieving (MRP) method to bridge the gap between the knowledge of VLMs and target learning tasks. Experimental results demonstrate the effectiveness of the proposed TFL setting and MRP learning method. The code to reproduce the experiments is at https://github.com/Tranquilxu/TMP.
Behavior-agnostic Task Inference for Robust Offline In-context Reinforcement Learning
Authors: Long Ma; Fangwei Zhong; Yizhou Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The ability to adapt to new environments with noisy dynamics and unseen objectives is crucial for AI agents. In-context reinforcement learning (ICRL) has emerged as a paradigm to build adaptive policies, employing a **context** trajectory of the test-time interactions to infer the true task and the corresponding optimal policy efficiently without gradient updates. However, ICRL policies heavily rely on context trajectories, making them vulnerable to distribution shifts from training to testing and degrading performance, particularly in offline settings where the training data is static. In this paper, we highlight that most existing offline ICRL methods are trained for approximate Bayesian inference based on the training distribution, rendering them vulnerable to distribution shifts at test time and resulting in poor generalization. To address this, we introduce Behavior-agnostic Task Inference (BATI) for ICRL, a model-based maximum-likelihood solution to infer the task representation robustly. In contrast to previous methods that rely on a learned encoder as the approximate posterior, BATI focuses purely on dynamics, thus insulating itself against the behavior of the context collection policy. Experiments on MuJoCo environments demonstrate that BATI effectively interprets out-of-distribution contexts and outperforms other methods, even in the presence of significant environmental noise.
Going Deeper into Locally Differentially Private Graph Neural Networks
Authors: Longzhu He; Chaozhuo Li; Peng Tang; Sen Su
Venue: ICML 2025 (ICML 2025 oral)

Abstract: Graph Neural Networks (GNNs) have demonstrated superior performance in a variety of graph mining and learning tasks. However, when node representations involve sensitive personal information or variables related to individuals, learning from graph data can raise significant privacy concerns. Although recent studies have explored local differential privacy (LDP) to address these concerns, they often introduce significant distortions to graph data, severely degrading private learning utility (e.g., node classification accuracy). In this paper, we present UPGNET, an LDP-based privacy-preserving graph learning framework that enhances utility while protecting user data privacy. Specifically, we propose a three-stage pipeline that generalizes the LDP protocols for node features, targeting privacy-sensitive scenarios. Our analysis identifies two key factors that affect the utility of privacy-preserving graph learning: *feature dimension* and *neighborhood size*. Based on the above analysis, UPGNET enhances utility by introducing two core layers: High-Order Aggregator (HOA) layer and the Node Feature Regularization (NFR) layer. Extensive experiments on real-world datasets indicate that UPGNET significantly outperforms existing methods in terms of both privacy protection and learning utility.
Reward Translation via Reward Machine in Semi-Alignable MDPs
Authors: Yun Hua; Haosheng Chen; Wenhao Li; Bo Jin; Baoxiang Wang; Hongyuan Zha; Xiangfeng Wang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Addressing reward design complexities in deep reinforcement learning is facilitated by knowledge transfer across different domains. To this end, we define \textit{reward translation} to describe the cross-domain reward transfer problem. However, current methods struggle with non-pairable and non-time-alignable incompatible MDPs.
This paper presents an adaptable reward translation framework \textit{neural reward translation} featuring \textit{semi-alignable MDPs}, which allows efficient reward translation under relaxed constraints while handling the intricacies of incompatible MDPs. Given the inherent difficulty of directly mapping semi-alignable MDPs and transferring rewards, we introduce an indirect mapping method through reward machines, created using limited human input or LLM-based automated learning.
Graph-matching techniques establish links between reward machines from distinct environments, thus enabling cross-domain reward translation within semi-alignable MDP settings. This broadens the applicability of DRL across multiple domains. 
Experiments substantiate our approach's effectiveness in tasks under environments with semi-alignable MDPs.
Uniform Mean Estimation for Heavy-Tailed Distributions via Median-of-Means
Authors: Mikael Møller Høgsgaard; Andrea Paudice
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The Median of Means (MoM) is a mean estimator that has gained popularity in the context of heavy-tailed data. In this work, we analyze its performance in the task of simultaneously estimating the mean of each function in a class $\mathcal{F}$ when the data distribution possesses only the first $p$ moments for $p \in (1,2]$. We prove a new sample complexity bound using a novel symmetrization technique that may be of independent interest. Additionally, we present applications of our result to $k$-means clustering with unbounded inputs and linear regression with general losses, improving upon existing works.
UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model
Authors: Timo Kaiser; Thomas Norrenbrock; Bodo Rosenhahn
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The introduction of the Segment Anything Model (SAM) has paved the way for numerous semantic segmentation applications. For several tasks, quantifying the uncertainty of SAM is of particular interest. However, the ambiguous nature of the class-agnostic foundation model SAM challenges current uncertainty quantification (UQ) approaches. This paper presents a theoretically motivated uncertainty quantification model based on a Bayesian entropy formulation jointly respecting aleatoric, epistemic, and the newly introduced task uncertainty. We use this formulation to train USAM, a lightweight post-hoc UQ method. Our model traces the root of uncertainty back to under-parameterised models, insufficient prompts or image ambiguities. Our proposed deterministic USAM demonstrates superior predictive capabilities on the SA-V, MOSE, ADE20k, DAVIS, and COCO datasets, offering a computationally cheap and easy-to-use UQ alternative that can support user-prompting, enhance semi-supervised pipelines, or balance the tradeoff between accuracy and cost efficiency.
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
Authors: Roman Bachmann; Jesse Allardice; David Mizrahi; Enrico Fini; Oğuzhan Fatih Kar; Elmira Amirloo; Alaaeldin El-Nouby; Amir Zamir; Afshin Dehghan
Venue: ICML 2025 (ICML 2025 poster)

Abstract: We introduce FlexTok, a tokenizer that projects 2D images into variable-length, ordered 1D token sequences. For example, a 256x256 image can be resampled into anywhere from 1 to 256 discrete tokens, hierarchically and semantically compressing its information. By training a rectified flow model as the decoder and using nested dropout, FlexTok produces plausible reconstructions regardless of the chosen token sequence length. We evaluate our approach in an autoregressive generation setting using a simple GPT-style Transformer. On ImageNet, this approach achieves an FID<2 across 8 to 128 tokens, outperforming TiTok and matching state-of-the-art methods with far fewer tokens. We further extend the model to support to text-conditioned image generation and examine how FlexTok relates to traditional 2D tokenization. A key finding is that FlexTok enables next-token prediction to describe images in a coarse-to-fine "visual vocabulary", and that the number of tokens to generate depends on the complexity of the generation task.
OW-VAP: Visual Attribute Parsing for Open World Object Detection
Authors: Xing Xi; Xing Fu; Weiqiang Wang; Ronghua Luo
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Open World Object Detection (OWOD) requires the detector to continuously identify and learn new categories. Existing methods rely on the large language model (LLM) to describe the visual attributes of known categories and use these attributes to mark potential objects. The performance of such methods is influenced by the accuracy of LLM descriptions, and selecting appropriate attributes during incremental learning remains a challenge. In this paper, we propose a novel OWOD framework, termed OW-VAP, which operates independently of LLM and requires only minimal object descriptions to detect unknown objects. Specifically, we propose a Visual Attribute Parser (VAP) that parses the attributes of visual regions and assesses object potential based on the similarity between these attributes and the object descriptions. To enable the VAP to recognize objects in unlabeled areas, we exploit potential objects within background regions.  Finally, we propose Probabilistic Soft Label Assignment (PSLA) to prevent optimization conflicts from misidentifying background as foreground. Comparative results on the OWOD benchmark demonstrate that our approach surpasses existing state-of-the-art methods with a +13 improvement in U-Recall and a +8 increase in U-AP for unknown detection capabilities. Furthermore, OW-VAP approaches the unknown recall upper limit of the detector.
Optimizing Large Language Model Training Using FP4 Quantization
Authors: Ruizhe Wang; Yeyun Gong; Xiao Liu; Guoshuai Zhao; Ziyue Yang; Baining Guo; Zheng-Jun Zha; Peng CHENG
Venue: ICML 2025 (ICML 2025 poster)

Abstract: The growing computational demands of training large language models (LLMs) necessitate more efficient methods. Quantized training presents a promising solution by enabling low-bit arithmetic operations to reduce these costs. While FP8 precision has demonstrated feasibility, leveraging FP4 remains a challenge due to significant quantization errors and limited representational capacity. This work introduces the first FP4 training framework for LLMs, addressing these challenges with two key innovations: a differentiable quantization estimator for precise weight updates and an outlier clamping and compensation strategy to prevent activation collapse. To ensure stability, the framework integrates a mixed-precision training scheme and vector-wise quantization. Experimental results demonstrate that our FP4 framework achieves accuracy comparable to BF16 and FP8, with minimal degradation, scaling effectively to 13B-parameter LLMs trained on up to 100B tokens. With the emergence of next-generation hardware supporting FP4, our framework sets a foundation for efficient ultra-low precision training.
SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
Authors: Guoxuan Chen; Han Shi; Jiawei Li; Yihang Gao; Xiaozhe Ren; Yimeng Chen; Xin Jiang; Zhenguo Li; Weiyang Liu; Chao Huang
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless separator tokens (i.e., punctuations) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM's effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.
G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks
Authors: Guibin Zhang; Yanwei Yue; Xiangguo Sun; Guancheng Wan; Miao Yu; Junfeng Fang; Kun Wang; Tianlong Chen; Dawei Cheng
Venue: ICML 2025 (ICML 2025 spotlightposter)

Abstract: Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite the diverse and high-performing designs available, practitioners often face confusion when selecting the most effective pipeline for their specific task: \textit{Which topology is the best choice for my task, avoiding unnecessary communication token overhead while ensuring high-quality solution?} In response to this dilemma, we introduce G-Designer, an adaptive, efficient, and robust solution for multi-agent deployment, which dynamically designs task-aware, customized communication topologies. Specifically, G-Designer models the multi-agent system as a multi-agent network, leveraging a variational graph auto-encoder to encode both the nodes (agents) and a task-specific virtual node, and decodes a task-adaptive and high-performing communication topology. Extensive experiments on six benchmarks showcase that G-Designer is: \textbf{(1) high-performing}, achieving superior results on MMLU with accuracy at $84.50\\%$ and on HumanEval with pass@1 at $89.90\\%$; \textbf{(2) task-adaptive}, architecting communication protocols tailored to task difficulty, reducing token consumption by up to $95.33\\%$ on HumanEval; and \textbf{(3) adversarially robust}, defending against agent adversarial attacks with merely $0.3\\%$ accuracy drop.
Heterogeneous Data Game: Characterizing the Model Competition Across Multiple Data Sources
Authors: Renzhe Xu; Kang Wang; Bo Li
Venue: ICML 2025 (ICML 2025 poster)

Abstract: Data heterogeneity across multiple sources is common in real-world machine learning (ML) settings. Although many methods focus on enabling a single model to handle diverse data, real-world markets often comprise multiple competing ML providers. In this paper, we propose a game-theoretic framework—the Heterogeneous Data Game—to analyze how such providers compete across heterogeneous data sources. We investigate the resulting pure Nash equilibria (PNE), showing that they can be non-existent, homogeneous (all providers converge on the same model), or heterogeneous (providers specialize in distinct data sources). Our analysis spans monopolistic, duopolistic, and more general markets, illustrating how factors such as the ``temperature'' of data-source choice models and the dominance of certain data sources shape equilibrium outcomes. We offer theoretical insights into both homogeneous and heterogeneous PNEs, guiding regulatory policies and practical strategies for competitive ML marketplaces.