Extreme Bandits using Robust Statistics
Machine Learning
2021-09-10 v1 Machine Learning
Abstract
We consider a multi-armed bandit problem motivated by situations where only the extreme values, as opposed to expected values in the classical bandit setting, are of interest. We propose distribution free algorithms using robust statistics and characterize the statistical properties. We show that the provided algorithms achieve vanishing extremal regret under weaker conditions than existing algorithms. Performance of the algorithms is demonstrated for the finite-sample setting using numerical experiments. The results show superior performance of the proposed algorithms compared to the well known algorithms.
Cite
@article{arxiv.2109.04433,
title = {Extreme Bandits using Robust Statistics},
author = {Sujay Bhatt and Ping Li and Gennady Samorodnitsky},
journal= {arXiv preprint arXiv:2109.04433},
year = {2021}
}