{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multi-objective-good-arm-identification-with","title":"Multi-objective Good Arm Identification with Bandit Feedback","arxiv_id":"2503.10386","date":"2025-03-13","proceeding":null,"authors":["Xuanke Jiang","Kohei Hatano","Eiji Takimoto"],"abstract":"We consider a good arm identification problem in a stochastic bandit setting with multi-objectives, where each arm $i\\in[K]$ is associated with $M$ distributions $\\mathcal{D}_i^{(1)}, \\ldots, \\mathcal{D}_i^{(M)}$. For each round $t$, the player/algorithm pulls one arm $i_t$ and receives a vector feedback, where each component $m$ is sampled according to $\\mathcal{D}_i^{(m)}$. The target is twofold, one is finding one arm whose means are larger than the predefined thresholds $\\xi_1,\\ldots,\\xi_M$ with a confidence bound $\\delta$ and an accuracy rate $\\epsilon$ with a bounded sample complexity, the other is output $\\bot$ to indicate no such arm exists. We propose an algorithm with a sample complexity bound. When $M=1$ and $\\epsilon = 0$, our bound is the same as the one given in the previous work when and novel bounds for $M > 1$. The proposed algorithm attains better numerical performance than other baselines in the experiments on synthetic and real datasets.","url_abs":"https://arxiv.org/abs/2503.10386v1","url_pdf":"https://arxiv.org/pdf/2503.10386v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multi-objective-good-arm-identification-with","repo_url":"https://github.com/2015211217/MultiThresholdBandit","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}