Low-Rank Bandits via Tight Two-to-Infinity Singular Subspace Recovery

arXiv:2402.15739v1 Announce Type: new
Abstract: We study contextual bandits with low-rank structure where, in each round, if the (context, arm) pair $(i,j)\in [m]\times [n]$ is selected, the learner observes a noisy sample of the $(i,j)$-th entry of an unknown low-rank reward matrix. Successive contexts are generated randomly in an i.i.d. manner and are revealed to the learner. For such bandits, we present efficient algorithms for policy evaluation, best policy identification and regret minimization. For policy evaluation and best policy identification, …

abstract arm arxiv context cs.lg generated low matrix recovery sample singular stat.ml study type via