Dynamic Regret Reduces to Kernelized Static Regret

Andrew Jacobsen, Alessandro Rudi, Francesco Orabona +1

We study dynamic regret in online convex optimization, where the objective is to achieve low cumulative loss relative to an arbitrary benchmark sequence. By observing that competin…