Feb. 15, 2024, 5:41 a.m. | Arun Suggala, Y. Jennifer Sun, Praneeth Netrapalli, Elad Hazan

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.08929v1 Announce Type: new
Abstract: Bandit convex optimization (BCO) is a general framework for online decision making under uncertainty. While tight regret bounds for general convex losses have been established, existing algorithms achieving these bounds have prohibitive computational costs for high dimensional data.
In this paper, we propose a simple and practical BCO algorithm inspired by the online Newton step algorithm. We show that our algorithm achieves optimal (in terms of horizon) regret bounds for a large class of convex …

abstract algorithms arxiv computational control costs cs.lg data decision decision making framework general losses making optimization paper practical simple stat.ml type uncertainty

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