@inproceedings{54532e5105f04fb4a65bbb0296e7c6c4,
title = "Better Best of Both Worlds Bounds for Bandits with Switching Costs",
abstract = "We study best-of-both-worlds algorithms for bandits with switching cost, recently addressed by Rouyer, Seldin, and Cesa-Bianchi [14]. We introduce a surprisingly simple and effective algorithm that simultaneously achieves minimax optimal regret bound (up to logarithmic factors) of O(T2/3) in the oblivious adversarial setting and a bound of O(min{log(T)/∆2,T2/3}) in the stochastically-constrained regime, both with (unit) switching costs, where ∆ is the gap between the arms. In the stochastically constrained case, our bound improves over previous results due to [14], that achieved regret of O(T1/3/∆). We accompany our results with a lower bound showing that, in general, {\~Ω}(min{1/∆2,T2/3}) switching cost regret is unavoidable in the stochastically-constrained case for algorithms with O(T2/3) worst-case switching cost regret.",
author = "Idan Amir and Guy Azov and Tomer Koren and Roi Livni",
note = "Publisher Copyright: {\textcopyright} 2022 Neural information processing systems foundation. All rights reserved.; 36th Conference on Neural Information Processing Systems, NeurIPS 2022 ; Conference date: 28-11-2022 Through 09-12-2022",
year = "2022",
language = "الإنجليزيّة",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
editor = "S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh",
booktitle = "Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022",
address = "الولايات المتّحدة",
}