Poster

Tight Regret Bounds for Infinite-armed Linear Contextual Bandits

Yingkai Li · Yining Wang · Xi Chen · Yuan Zhou

Keywords: [ Learning Theory and Statistics ] [ Decision Processes and Bandits ]

[ Abstract ]
Tue 13 Apr 6:30 p.m. PDT — 8:30 p.m. PDT

Abstract:

Linear contextual bandit is a class of sequential decision-making problems with important applications in recommendation systems, online advertising, healthcare, and other machine learning-related tasks. While there is much prior research, tight regret bounds of linear contextual bandit with infinite action sets remain open. In this paper, we consider the linear contextual bandit problem with (changing) infinite action sets. We prove a regret upper bound on the order of O(\sqrt{d^2T\log T}) \poly(\log\log T) where d is the domain dimension and T is the time horizon. Our upper bound matches the previous lower bound of \Omega(\sqrt{d^2 T\log T}) in [Li et al., 2019] up to iterated logarithmic terms.

Chat is not available.