PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Strategies for prediction under imperfect monitoring
Gábor Lugosi, Gilles Stoltz and Shie Mannor
In: COLT 2007, 13-15 June 2007, San Diego, CA, USA.

Abstract

We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. It was Rustichini (1999) who first proved the existence of such consistent predictors. The forecasters presented here offer the first constructive proof of consistency. Moreover, the proposed algorithms are computationally efficient. We also establish upper bounds for the rates of convergence. In the case of deterministic feedback, these rates are optimal up to logarithmic terms.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3172
Deposited By:Gilles Stoltz
Deposited On:03 January 2008