Feb. 13, 2024, 5:45 a.m. | Gianmarco Genalti Lupo Marsigli Nicola Gatti Alberto Maria Metelli

cs.LG updates on arXiv.org arxiv.org

Heavy-tailed distributions naturally arise in several settings, from finance to telecommunications. While regret minimization under subgaussian or bounded rewards has been widely studied, learning with heavy-tailed distributions only gained popularity over the last decade. In this paper, we consider the setting in which the reward distributions have finite absolute raw moments of maximum order $1+\epsilon$, uniformly bounded by a constant $u<+\infty$, for some $\epsilon \in (0,1]$. In this setting, we study the regret minimization problem when $\epsilon$ and $u$ are …

cs.ai cs.lg finance moments paper raw telecommunications

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