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Achieving Near-Optimal Regret for Bandit Algorithms with Uniform Last-Iterate Guarantee
Feb. 21, 2024, 5:42 a.m. | Junyan Liu, Yunfan Li, Lin Yang
cs.LG updates on arXiv.org arxiv.org
Abstract: Existing performance measures for bandit algorithms such as regret, PAC bounds, or uniform-PAC (Dann et al., 2017), typically evaluate the cumulative performance, while allowing the play of an arbitrarily bad arm at any finite time t. Such a behavior can be highly detrimental in high-stakes applications. This paper introduces a stronger performance measure, the uniform last-iterate (ULI) guarantee, capturing both cumulative and instantaneous performance of bandit algorithms. Specifically, ULI characterizes the instantaneous performance since it …
abstract algorithms arm arxiv behavior cs.lg iterate near performance stat.ml type uniform
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