Efficient Algorithm with No-Regret Bound for Sleeping Expert Problem

Abstract

The sleeping experts problem is a variant of decision-theoretic online learning (DTOL) where the set of available experts may change over time. In this thesis, we study a special case of the sleeping experts problem with constraints on how the set of available experts can change. The benchmark we use is ranking regret, which is a common benchmark used in sleeping experts problem. Previous research shows that achieving sub-linear ranking regret bound in the general sleeping experts problem is NP-hard, so we relax the sleeping experts problem by imposing constraints on how the set of available experts may change. Under those constraints, we present an efficient algorithm which achieves a sub-linear ranking regret bound.