{"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/mabwiser-a-parallelizable-contextual-multi","title":"MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python","arxiv_id":null,"date":"2019-10-04","proceeding":"IEEE 31th International Conference on Tools with Artificial Intelligence, ICTAI 2019 2019 10","authors":["Emily Strong","Bernard Kleynhans","Serdar Kadioglu"],"abstract":"Contextual multi-armed bandit algorithms serve as an effective technique to address online sequential decision-making problems. Despite their popularity, when it comes to off-the-shelf tools the library support remains limited, in particular for the Python technology stack. To fill this gap, in this paper we present a system that provides context-free, parametric and nonparametric contextual multi-armed bandit models. The available bandit policies accommodate both batch and online learning. The MABWISER system is implemented as an open-source Python library. Our design enables built-in parallelization to speed up training and test components for scalability while ensuring the reproducibility of results. We present a running example to highlight the user-friendly nature of the public interface and discuss the simulation capability of the library for hyperparameter tuning and rapid experimentation.","url_abs":"http://www.ictai2019.org/","url_pdf":"http://www.ictai2019.org/","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":"mabwiser-a-parallelizable-contextual-multi","repo_url":"https://github.com/fmr-llc/mabwiser","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"sequential-decision-making","task_name":"Sequential Decision Making"}],"methods":[{"method_slug":"speed","method_name":"SPEED"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}