Adaptive Online Prediction by Following the Perturbed Leader

Details Adaptive Online Prediction by Following the Perturbed Leader Adaptive Online Prediction by Following the Perturbed Leader

2005-04-16

arxiv.org/abs/cs/0504078v1

Journal of Machine Learning Research 6 (2005) 639--660

Computer Science

Artificial Intelligence

25 pages

Scientific paper

When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. The analysis of the alternative "Follow the Perturbed Leader" (FPL) algorithm from Kalai & Vempala (2003) (based on Hannan's algorithm) is easier. We derive loss bounds for adaptive learning rate and both finite expert classes with uniform weights and countable expert classes with arbitrary weights. For the former setup, our loss bounds match the best known results so far, while for the latter our results are new.

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