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Prediction by random-walk perturbation
This video was recorded at 26th Annual Conference on Learning Theory (COLT), Princeton 2013. We propose a version of the follow-the-perturbed-leader online prediction algorithm in which the cumulative losses are perturbed by independent symmetric random walks. The forecaster is shown to achieve an expected regret of the optimal order O(√nlogN) where n is the time horizon and N is the number of experts. More importantly, it is shown that the forecaster changes its prediction at most O(√nlogN) times, in expectation. We also extend the analysis to online combinatorial optimization and show that even in this more general setting, the forecaster rarely switches between experts while having a regret of near-optimal order.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
