Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This can be problematic when contributors are dependent (e.g., reused/augmented data or causal feature orderings) or when contributions should be adjusted by factors such as trust or risk. We propose Priority-Aware Shapley Value (PASV), which incorporates both hard precedence constraints and soft, contributor-specific priority weights. PASV is applicable to general precedence structures, recovers precedence-only and weight-only Shapley variants as special cases, and is uniquely characterized by natural axioms. We develop an efficient adjacent-swap Metropolis-Hastings sampler for scalable Monte Carlo estimation and analyze limiting regimes induced by extreme priority weights. Experiments on data valuation (MNIST/CIFAR10) and feature attribution (Census Income) demonstrate more structure-faithful allocations and a practical sensitivity analysis via our proposed "priority sweeping".
Fairness-Aware Federated Learning with Trajectory Shapley Value
Faculty of Mathematics, Ecole Normale Supérieure Paris-Saclay（巴黎-萨克雷大学数学系）；Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学数学系）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Snap Inc.（色拉布公司）
Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for GNN explanation, where the task is to identify the most important subgraph and constituent nodes for GNN predictions. We claim that the Shapley value is a non-ideal choice for graph data because it is by definition…
Uncertainty-Aware Multimodal Learning via Conformal Shapley Intervals
Department of Statistics and Data Science, UCLA（统计与数据科学系， UCLA）；Merck & Co（默克公司）；Department of Biostatistics, UCLA（生物统计学系， UCLA）；Departments of Statistics and Data Science, and of Biostatistics, UCLA（统计与数据科学系以及生物统计学系， UCLA）
Temporal-Decay Shapley: A Time-Aware Data Valuation Framework for Time-Series Data
School of Artificial Intelligence, Guangzhou University（广州大学人工智能学院）；School of Public Finance and Taxation, Guangdong University of Finance and Economics（广东财经大学公共财政与税收学院）
ETH Zürich（苏黎世联邦理工学院）；MPI-IS（马克斯·普朗克智能系统研究所）；KU Leuven（鲁汶大学）
Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases with the number of these lines. In this work, we present the new problem of optimizing the positioning of lidar lines to find the most effective configuration for the depth completion task. We propose a solution to reduce the number of lines while retaining the up-to-the-mark quality of depth completion. Our method consists of two components, (1) line selection based on the marginal contribution of a line computed vi…
Multi-label classification is a type of classification task, it is used when there are two or more classes, and the data point we want to predict may belong to none of the classes or all of them at the same time. In the real world, many applications are actually multi-label involved, including information retrieval, multimedia content annotation, web mining, and so on. A game theory-based framework known as SHapley Additive exPlanations (SHAP) has been applied to explain various supervised learning models without being aware of the exact model. Herein, this work further extends the explanation…
RankMap: Priority-Aware Multi-DNN Manager for Heterogeneous Embedded Devices
School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University（南伊利诺伊大学电气、计算机与生物医学工程学院）；Department of Electrical and Computer Engineering, The University of Texas at Austin（德克萨斯大学奥斯汀分校电气与计算机工程系）
Mohamed bin Zayed University of Artificial Intelligence（莫萨德·本·泽德人工智能大学）；University at Buffalo（布法罗大学）；King Abdullah University of Science and Technology（卡布斯国王科学与技术大学）
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications（网络系统架构与融合北京市重点实验室，北京邮电大学）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）
Exact Shapley Attributions in Quadratic-time for FANOVA Gaussian Processes
Department of Computer Science, Vrije Universiteit Amsterdam, The Netherlands（荷兰阿姆斯特丹自由大学计算机科学系）；Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Germany（德国信息安全部CISPA理性智能实验室）；College of Computing & Data Science, Nanyang Technological University, Singapore（新加坡南洋理工大学计算与数据科学学院）
History-Aware and Dynamic Client Contribution in Federated Learning
Max Planck Institute for Software Systems, Germany（马克斯·普朗克软件系统研究所，德国）；Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStAL, France（里尔大学，法国）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore（高性能计算研究所（IHPC），新加坡）；EVYD Technology, Singapore（EVYD技术公司，新加坡）
Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning
King Abdullah University of Science and Technology（卡布尔大学科学与技术大学）；Chinese Academy of Sciences, Institute of Automation（中国科学院自动化研究所）；AI Centre, Department of Computer Science, University College London（伦敦大学学院人工智能中心）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究所）
A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty
Leibniz Institute for Prevention Research and Epidemiology – BIPS（莱布尼茨预防研究与流行病学研究所——BIPS）；University of Bremen（不来梅大学）；Simula Research Laboratory（西穆拉研究实验室）；Telenor Research & Innovation（挪威电信研究与创新部）；Norwegian Computing Center（挪威计算中心）
On-device Content-based Recommendation with Single-shot Embedding Pruning: A Cooperative Game Perspective
The University of Queensland（昆士兰大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Griffith University（格里菲斯大学）；University of Electronic Science and Technology of China（电子科技大学）
Louisiana State University（路易斯安那州立大学）；The Ohio State University（俄亥俄州立大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Data modeling and reduction for in situ is important. Feature-driven methods for in situ data analysis and reduction are a priority for future exascale machines as there are currently very few such methods. We investigate a deep-learning based workflow that targets in situ data processing using autoencoders. We propose a Residual Autoencoder integrated Residual in Residual Dense Block (RRDB) to obtain better performance. Our proposed framework compressed our test data into 66 KB from 2.1 MB per 3D volume timestep.
