  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.

Machine Learning;Computer Science and Game Theory