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

Yihan Zhou, Victor S. Portella, Mark Schmidt +1

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…