Online Learning: Random Averages, Combinatorial Parameters, and Learnability

Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari

Abstract: We develop a theory of online learning by defining several complexity measures. Among them are analogues of Rademacher complexity, covering numbers and fat-shattering dimension from statistical learning theory. Relationship among these complexity measures, their connection to online learning, and tools for bounding them are provided. We apply these results to various learning problems. We provide a complete characterization of online learnability in the supervised setting.