Non-stationary Bandits with Heavy Tail

被引：0
|
作者：
Pan, Weici [1 ]
Liu, Zhenhua [1 ]
机构：
[1] Stony Brook University, United States
来源：
Performance Evaluation Review | 2024年 / 52卷 / 02期
关键词：
Gaussian assumption - Heavy-tailed - Heavy-tails - Multiarmed bandits (MABs) - Nonstationary - Performance - Risk neutrals - Sub-Gaussians;
D O I：
10.1145/3695411.3695424
中图分类号：
学科分类号：
摘要：
In this study, we investigate the performance of multi-armed bandit algorithms in environments characterized by heavytailed and non-stationary reward distributions, a setting that deviates from the conventional risk-neutral and sub- Gaussian assumptions. © 2024 Copyright is held by the owner/author(s).
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页码：33 / 35
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