Prediction with Expert Advice by Following the Perturbed Leader for General Weights

When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of 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 [KV03] (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. Keywords Prediction with Expert Advice, Follow the Perturbed Leader, general weights, adaptive learning rate, hierarchy of experts, expected and high probability bounds, general alphabet and loss, online sequential prediction, ∗ This work was supported by SNF grant 2100-6771...

• Type: Conference
• Year: 2004
• Where: ALT
• Authors: Marcus Hutter, Jan Poland
• Keywords: Adaptive Learning Rate, ALT 2004, Arbitrary Weights, Loss Bounds, Machine Learning