Title: Online Improper Learning with an Approximation Oracle,
Abstract: We revisit the question of reducing online learning to approximate
optimization of the offline problem. In this setting, we give two algorithms
with near-optimal performance in the full information setting: they guarantee
optimal regret and require only poly-logarithmically many calls to the
approximation oracle per iteration. Furthermore, these algorithms apply to the
more general improper learning problems. In the bandit setting, our algorithm
also significantly improves the best previously known oracle complexity while
maintaining the same regret.