Structural Online Learning

We study the problem of learning ensembles in the online setting, when the hypotheses are selected out of a base family that may be a union of possibly very complex sub-families. We prove new theoretical guarantees for the online learning of such ensembles in terms of the sequential Rademacher complexities of these sub-families. We also describe an algorithm that benefits from such guarantees. We further extend our framework by proving new structural estimation error guarantees for ensembles in the batch setting through a new data-dependent online-tobatch conversion technique, thereby also devising an effective algorithm for the batch setting which does not require the estimation of the Rademacher complexities of base sub-families.