{"ID":2848564,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.25811","arxiv_id":"2510.25811","title":"Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms","abstract":"We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn enables the implementation of asymptotically optimal algorithms for this bandit problem. The code for the proposed algorithms is publicly available at https://github.com/wilrev/MultimodalBandits","short_abstract":"We consider a stochastic multi-armed bandit problem with i.i.d. rewards where the expected reward function is multimodal with at most m modes. We propose the first known computationally tractable algorithm for computing the solution to the Graves-Lai optimization problem, which in turn enables the implementation of asy...","url_abs":"https://arxiv.org/abs/2510.25811","url_pdf":"https://arxiv.org/pdf/2510.25811v1","authors":"[\"William Réveillard\",\"Richard Combes\"]","published":"2025-10-29T12:32:07Z","proceeding":"stat.ML","tasks":"[\"stat.ML\",\"cs.LG\",\"math.ST\"]","methods":"[]","has_code":false,"code_links":[{"ID":607632,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2848564,"paper_url":"https://arxiv.org/abs/2510.25811","paper_title":"Multimodal Bandits: Regret Lower Bounds and Optimal Algorithms","repo_url":"https://github.com/wilrev/MultimodalBandits","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}