DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation

Understand

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.