Computer Science > Machine Learning
[Submitted on 12 Feb 2016 (v1), last revised 4 Nov 2016 (this version, v4)]
Title:Coin Betting and Parameter-Free Online Learning
View PDFAbstract:In the recent years, a number of parameter-free algorithms have been developed for online linear optimization over Hilbert spaces and for learning with expert advice. These algorithms achieve optimal regret bounds that depend on the unknown competitors, without having to tune the learning rates with oracle choices.
We present a new intuitive framework to design parameter-free algorithms for \emph{both} online linear optimization over Hilbert spaces and for learning with expert advice, based on reductions to betting on outcomes of adversarial coins. We instantiate it using a betting algorithm based on the Krichevsky-Trofimov estimator. The resulting algorithms are simple, with no parameters to be tuned, and they improve or match previous results in terms of regret guarantee and per-round complexity.
Submission history
From: Francesco Orabona [view email][v1] Fri, 12 Feb 2016 17:11:42 UTC (34 KB)
[v2] Mon, 27 Jun 2016 20:16:44 UTC (57 KB)
[v3] Fri, 28 Oct 2016 16:43:55 UTC (57 KB)
[v4] Fri, 4 Nov 2016 01:30:29 UTC (57 KB)
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