Active Nonparametric Two-Sample Testing by Betting on Heterogeneous Data Sources

We study the problem of active nonparametric sequential two-sample testing over multiple heterogeneous data sources. In each time slot, a decision-maker adaptively selects one of $K$ data sources and receives a paired sample generated from that source for testing. The goal is to decide as quickly as possible whether the pairs are generated from the same distribution or not. The gain achieved by such adaptive sampling (in terms of smaller expected stopping time or larger error exponents) has been well-characterized for parametric models via Chernoff's adaptive MLE selection rule [1]. However, analogous results are not known for the case of nonparametric problems, such as two-sample testing, where we place no restrictions on the distributions. Our main contribution is a general active nonparametric testing procedure that combines an adaptive source-selecting strategy within the testing-by-betting framework of [2] that works under minimal distributional assumptions. In each time slot, our scheme proceeds by selecting a source according to a probability that mixes exploitation, favoring sources with the largest empirical distinguishability, and exploration via a vanishing greedy strategy. The (paired) observations so collected are then used to update the "betting-wealth process", which is a stochastic process guaranteed to be a nonnegative martingale under the null. The procedure stops and rejects the null when the wealth process exceeds an appropriate threshold; an event that is unlikely under the null. We show that our test controls the type-I error at a prespecified level-$α$ under the null, and establish its power-one property and a bound on its expected sample size under the alternative. Our results provide a precise characterization of the improvements achievable by a principled adaptive sampling strategy over its passive analog.