  We consider the dataset valuation problem, that is, the problem of
quantifying the incremental gain, to some relevant pre-defined utility of a
machine learning task, of aggregating an individual dataset to others. The
Shapley value is a natural tool to perform dataset valuation due to its formal
axiomatic justification, which can be combined with Monte Carlo integration to
overcome the computational tractability challenges. Such generic approximation
methods, however, remain expensive in some cases. In this paper, we exploit the
knowledge about the structure of the dataset valuation problem to devise more
efficient Shapley value estimators. We propose a novel approximation, referred
to as discrete uniform Shapley, which is expressed as an expectation under a
discrete uniform distribution with support of reasonable size. We justify the
relevancy of the proposed framework via asymptotic and non-asymptotic
theoretical guarantees and illustrate its benefits via an extensive set of
numerical experiments.
