Shuichi Hirahara

Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits

We propose novel algorithms with first- and second-order regret bounds for adversarial linear bandits. These regret bounds imply that our algorithms perform well when there is an action achieving a small cumulative loss or the loss has a small variance. In addition, we need only assumptions weaker t…