Copeland Dueling Bandits
Masrour Zoghi, Zohar S Karnin, Shimon Whiteson, Maarten de Rijke
Abstract
A version of the dueling bandit problem is addressed in which a Condorcet winner may not exist. Two algorithms are proposed that instead seek to minimize regret with respect to the Copeland winner, which, unlike the Condorcet winner, is guaranteed to exist. The first, Copeland Confidence Bound (CCB), is designed for small numbers of arms, while the second, Scalable Copeland Bandits (SCB), works better for large-scale problems. We provide theoretical results bounding the regret accumulated by CCB and SCB, both substantially improving existing results. Such existing results either offer bounds of the form O(K log T) but require restrictive assumptions, or offer bounds of the form O(K^2 log T) without requiring such assumptions. Our results offer the best of both worlds: O(K log T) bounds without restrictive assumptions.
BibTeX
@inproceedings{NIPS2015_9872ed9f,
author = {Zoghi, Masrour and Karnin, Zohar S and Whiteson, Shimon and de Rijke, Maarten},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Copeland Dueling Bandits},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/9872ed9fc22fc182d371c3e9ed316094-Paper.pdf},
volume = {28},
year = {2015}
}