# Adaptive Hedge

@inproceedings{Erven2011AdaptiveH, title={Adaptive Hedge}, author={Tim van Erven and Peter Gr{\"u}nwald and Wouter M. Koolen and Steven de Rooij}, booktitle={NIPS}, year={2011} }

Most methods for decision-theoretic online learning are based on the Hedge algorithm, which takes a parameter called the learning rate. In most previous analyses the learning rate was carefully tuned to obtain optimal worst-case performance, leading to suboptimal performance on easy instances, for example when there exists an action that is significantly better than all others. We propose a new way of setting the learning rate, which adapts to the difficulty of the learning problem: in the…

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