{"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/scaling-multi-armed-bandit-algorithms","title":"Scaling Multi-Armed Bandit Algorithms","arxiv_id":null,"date":"2019-07-25","proceeding":"KDD 2019 7","authors":["Edouard Fouché","Junpei Komiyama","Klemens Böhm"],"abstract":"The Multi-Armed Bandit (MAB) is a fundamental model capturing\r\nthe dilemma between exploration and exploitation in sequential\r\ndecision making. At every time step, the decision maker selects a\r\nset of arms and observes a reward from each of the chosen arms.\r\nIn this paper, we present a variant of the problem, which we call\r\nthe Scaling MAB (S-MAB): The goal of the decision maker is not\r\nonly to maximize the cumulative rewards, i.e., choosing the arms\r\nwith the highest expected reward, but also to decide how many\r\narms to select so that, in expectation, the cost of selecting arms\r\ndoes not exceed the rewards. This problem is relevant to many real-\r\nworld applications, e.g., online advertising, financial investments\r\nor data stream monitoring. We propose an extension of Thompson\r\nSampling, which has strong theoretical guarantees and is reported\r\nto perform well in practice. Our extension dynamically controls the\r\nnumber of arms to draw. Furthermore, we combine the proposed\r\nmethod with ADWIN, a state-of-the-art change detector, to deal\r\nwith non-static environments. We illustrate the benefits of our\r\ncontribution via a real-world use case on predictive maintenance.","url_abs":"https://dl.acm.org/doi/10.1145/3292500.3330862","url_pdf":"https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://edouardfouche.com/publications/S-MAB_FOUCHE_KDD19.pdf&ved=2ahUKEwixw8HZvbGMAxXR9wIHHeuCM08QFnoECBQQAQ&usg=AOvVaw1WSvCiz4ITwfyLlxCFlrfT","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":"scaling-multi-armed-bandit-algorithms","repo_url":"https://github.com/PlaytikaOSS/pybandits","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"},{"task_slug":"thompson-sampling","task_name":"Thompson Sampling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}