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Online Agnostic Boosting via Regret Minimization

Author(s): Brukhim, Nataly; Chen, Xinyi; Hazan, Elad; Moran, Shay

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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.
Publication Date: 2020
Citation: Brukhim, Nataly, Xinyi Chen, Elad Hazan, and Shay Moran. "Online Agnostic Boosting via Regret Minimization." Advances in Neural Information Processing Systems 33 (2020).
ISSN: 1049-5258
Type of Material: Conference Article
Journal/Proceeding Title: Advances in Neural Information Processing Systems
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.

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