{"paper":{"title":"Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Han Shao, Irwin King, Michael R. Lyu, Xiaotian Yu","submitted_at":"2018-10-25T14:29:02Z","abstract_excerpt":"In linear stochastic bandits, it is commonly assumed that payoffs are with sub-Gaussian noises. In this paper, under a weaker assumption on noises, we study the problem of \\underline{lin}ear stochastic {\\underline b}andits with h{\\underline e}avy-{\\underline t}ailed payoffs (LinBET), where the distributions have finite moments of order $1+\\epsilon$, for some $\\epsilon\\in (0,1]$. We rigorously analyze the regret lower bound of LinBET as $\\Omega(T^{\\frac{1}{1+\\epsilon}})$, implying that finite moments of order 2 (i.e., finite variances) yield the bound of $\\Omega(\\sqrt{T})$, with $T$ being the t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10895","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}