Date of this Version
Proceedings of the 16th International Conference on Artificial Intelligence and Statistics
We introduce a formalism of localization for online learning problems, which, similarly to statistical learning theory, can be used to obtain fast rates. In particular, we introduce local sequential Rademacher complexities and other local measures. Based on the idea of relaxations for deriving algorithms, we provide a template method that takes advantage of localization. Furthermore, we build a general adaptive method that can take advantage of the suboptimality of the observed sequence. We illustrate the utility of the introduced concepts on several problems. Among them is a novel upper bound on regret in terms of classical Rademacher complexity when the data are i.i.d.
Rakhlin, A., Shamir, O., & Sridharan, K. (2013). Localization and Adaptation in Online Learning. Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, 31 516-526. Retrieved from https://repository.upenn.edu/statistics_papers/130
Date Posted: 27 November 2017
This document has been peer reviewed.