Most Correlated Arms Identification

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Che-Yu Liu, Sébastien Bubeck ;
Proceedings of The 27th Conference on Learning Theory, PMLR 35:623-637, 2014.

Abstract

We study the problem of finding the most mutually correlated arms among many arms. We show that adaptive arms sampling strategies can have significant advantages over the non-adaptive uniform sampling strategy. Our proposed algorithms rely on a novel correlation estimator. The use of this accurate estimator allows us to get improved results for a wide range of problem instances.

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