Abstract:
Boosting is a widely used machine learning approach based on the idea of aggregating weak learning
rules. While in statistical learning numerous boosting methods exist both in the realizable and agnostic
settings, in online learning they exist only in the realizable case. In this work we provide the first agnostic
online boosting algorithm; that is, given a weak learner with only marginally-better-than-trivial regret
guarantees, our algorithm boosts it to a strong learner with sublinear regret.
Our algorithm is based on an abstract (and simple) reduction to online convex optimization, which
efficiently converts an arbitrary online convex optimizer to an online booster. Moreover, this reduction
extends to the statistical as well as the online realizable settings, thus unifying the 4 cases of statistical/online and agnostic/realizable boosting.
