Strategies for Prediction under Imperfect Monitoring
published: Aug. 26, 2009, recorded: June 2009, views: 3087
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We propose simple randomized strategies for sequential decision (or prediction) under imperfect monitoring, that is, when the decision maker (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 among all fixed actions. It was Rustichini who first proved the existence of such consistent predictors. The forecasters presented in this talk 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 signals, these rates are optimal up to logarithmic terms. (Joint work with Shie Mannor and Gilles Stoltz.)
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