Please note: This master’s thesis presentation will take place in DC 2314.
Junhao Lin, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Ian Munro
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 sublinear 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 sublinear ranking regret bound.