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：沙普利值可以说是合作博弈中为玩家赋予有意义贡献值最流行的方法，近年来在机器学习的各个领域被大量使用，尤其在可解释人工智能中最为突出。其意义来自于只有沙普利值满足的公理性质，但代价是精确计算随智能体数量呈指数增长。因此，许多工作致力于高效近似沙普利值，所有这些工作都围绕智能体的边际贡献概念展开。在本文中，我们提出了SVARM和分层SVARM两种无参数且领域无关的近似算法，它们基于一种脱离边际贡献概念的沙普利值表示。我们证明了这些算法在近似质量方面无与伦比的理论保证，并提供了令人满意的实证结果。