{"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/wax-ml-a-python-library-for-machine-learning","title":"WAX-ML: A Python library for machine learning and feedback loops on streaming data","arxiv_id":"2106.06524","date":"2021-06-11","proceeding":null,"authors":["Emmanuel Sérié"],"abstract":"Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data. It strives to complement JAX with tools dedicated to time series. WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation. It provides a simple mechanism for implementing feedback loops, allows the implementation of online learning and reinforcement learning algorithms with functions, and makes them easy to integrate by end-users working with the object-oriented reinforcement learning framework from the Gym library. It is released with an Apache open-source license on GitHub at https://github.com/eserie/wax-ml.","url_abs":"https://arxiv.org/abs/2106.06524v1","url_pdf":"https://arxiv.org/pdf/2106.06524v1.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":"wax-ml-a-python-library-for-machine-learning","repo_url":"https://github.com/eserie/wax-ml","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}