{"paper":{"title":"The True Sample Complexity of Identifying Good Arms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Julian Katz-Samuels, Kevin Jamieson","submitted_at":"2019-06-15T17:39:18Z","abstract_excerpt":"We consider two multi-armed bandit problems with $n$ arms: (i) given an $\\epsilon > 0$, identify an arm with mean that is within $\\epsilon$ of the largest mean and (ii) given a threshold $\\mu_0$ and integer $k$, identify $k$ arms with means larger than $\\mu_0$. Existing lower bounds and algorithms for the PAC framework suggest that both of these problems require $\\Omega(n)$ samples. However, we argue that these definitions not only conflict with how these algorithms are used in practice, but also that these results disagree with intuition that says (i) requires only $\\Theta(\\frac{n}{m})$ sampl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.06594","kind":"arxiv","version":1},"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"}