The Shapley value, which is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, has recently been used intensively in explainable artificial intelligence. Its meaningfulness is due to axiomatic properties that only the Shapley value satisfies, which, however, comes at the expense of an exact computation growing exponentially with the number of agents. Accordingly, a number of works are devoted to the efficient approximation of the Shapley value, most of them revolve around the notion of an agent's marginal contribution. In this paper, we propose with SVARM and Stratified SVARM two parameter-free and domain-independent approximation algorithms based on a representation of the Shapley value detached from the notion of marginal contribution. We prove unmatched theoretical guarantees regarding their approximation quality and provide empirical results including synthetic games as well as common explainability use cases comparing ourselves with state-of-the-art methods.
Towards Efficient Data Valuation Based on the Shapley Value
University of California at Berkeley（加州大学伯克利分校）；ETH, Zurich（苏黎世联邦理工学院）；Zhejiang University（浙江大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficiently
School of Computing（计算机学院）；National University of Singapore（新加坡国立大学）；School of Computer Science（计算机科学学院）；University of Waterloo（滑铁卢大学）；Vector Institute（向量研究院）
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）
Due to the intractable partition function, the exact likelihood function for a Markov random field (MRF), in many situations, can only be approximated. Major approximation approaches include pseudolikelihood and Laplace approximation. In this paper, we propose a novel way of approximating the likelihood function through first approximating the marginal likelihood functions of individual parameters and then reconstructing the joint likelihood function from these marginal likelihood functions. For approximating the marginal likelihood functions, we derive a particular likelihood function from a …
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）
Adalina: Adaptive Linear Approximation for the Shapley Value and Beyond
School of Computer Science, University of Waterloo, Canada（滑铁卢大学计算机科学学院）；Department of Computer Science, National University of Singapore, Republic of Singapore（新加坡国立大学计算机科学系）；Vector Institute, Canada（加拿大向量研究所）
Approximating Uniform Random Rotations by Two-Stage Structured Hadamard Rotations in High Dimensions
Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology（数据与决策科学学院，技术学院 – 以色列理工学院）；Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University（埃德华·P·菲茨工业与系统工程系，北卡罗来纳州立大学）
