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On fixed convex combinations of no-regret learners

Calliess, JP (2009) On fixed convex combinations of no-regret learners. In: UNSPECIFIED pp. 494-504..

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No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we investigate what happens when a set of no-regret learners (voters) merge their respective decisions in each learning iteration to a single, common one in form of a convex combination. We show that an agent (or algorithm) that executes this merged decision in each iteration of the online learning process and each time feeds back a copy of its own reward function to the voters, incurs sublinear regret itself. As a by-product, we obtain a simple method that allows us to construct new no-regret algorithms out of known ones. © 2009 Springer Berlin Heidelberg.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:05
Last Modified: 22 May 2018 08:13