Statistics Papers

Title

Competing With Strategies

Document Type

Conference Paper

Date of this Version

2013

Publication Source

JMLR: Workshop and Conference Proceedings

Volume

2013

Start Page

1

Last Page

27

Abstract

We study the problem of online learning with a notion of regret defined with respect to a set of strategies. We develop tools for analyzing the minimax rates and for deriving regret-minimization algorithms in this scenario. While the standard methods for minimizing the usual notion of regret fail, through our analysis we demonstrate existence of regret-minimization methods that compete with such sets of strategies as: autoregressive algorithms, strategies based on statistical models, regularized least squares, and follow the regularized leader strategies. In several cases we also derive efficient learning algorithms

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Date Posted: 27 November 2017

This document has been peer reviewed.