Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality
Machine Learning
2013-04-02 v3 Machine Learning
Abstract
We consider an original problem that arises from the issue of security analysis of a power system and that we name optimal discovery with probabilistic expert advice. We address it with an algorithm based on the optimistic paradigm and on the Good-Turing missing mass estimator. We prove two different regret bounds on the performance of this algorithm under weak assumptions on the probabilistic experts. Under more restrictive hypotheses, we also prove a macroscopic optimality result, comparing the algorithm both with an oracle strategy and with uniform sampling. Finally, we provide numerical experiments illustrating these theoretical findings.
Cite
@article{arxiv.1207.5259,
title = {Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality},
author = {Sebastien Bubeck and Damien Ernst and Aurelien Garivier},
journal= {arXiv preprint arXiv:1207.5259},
year = {2013}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1110.5447