The Shapley value is arguably the most popular approach for assigning a meaningful contribution value to players in a cooperative game, which has recently been used intensively in various areas of machine learning, most notably in explainable artificial intelligence. The 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 values, all of which 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 contributions. We prove unmatched theoretical guarantees regarding their approximation quality and provide satisfying empirical results.

翻译：Shapley值可以说是最受欢迎的方法,用于给合作游戏的参与者分配有意义的贡献价值,最近,在机器学习的各个领域,特别是在可解释的人工智能中,这种贡献最近被大量使用,其意义在于只满足Shapley值的不言而喻的特性,然而,这种特性是以精确计算随着代理人数目的成倍增长而付出的代价。因此,一些作品致力于有效接近Shapley值,所有这些都围绕着代理人的边缘贡献概念。在本文中,我们向SVARM和Steniz SVARM提出两种无参数和无域独立的近似算法,其依据是Shapley值与边际贡献概念脱钩的表示。我们证明关于这些价值的近似质量的理论保证是完全不相配的,并提供了令人满意的经验结果。