Most Correlated Arms Identification
Machine Learning
2014-04-24 v1 Machine Learning
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.
Cite
@article{arxiv.1404.5903,
title = {Most Correlated Arms Identification},
author = {Che-Yu Liu and Sébastien Bubeck},
journal= {arXiv preprint arXiv:1404.5903},
year = {2014}
}