Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses

摘要

In online convex optimization (OCO), Lipschitz continuity of the functions is commonly assumed in order to obtain sublinear regret. Moreover, many algorithms have only logarithmic regret when these functions are also strongly convex. Recently, researchers from convex optimization proposed the notions of