{"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/restarted-bayesian-online-change-point","title":"Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay","arxiv_id":null,"date":"2020-01-01","proceeding":"ICML 2020 1","authors":["REDA ALAMI","Odalric-Ambrym Maillard","Raphaël Féraud"],"abstract":"In this paper, we consider the problem of sequential change-point detection where both the change-points and the distributions before and after the change are assumed to be unknown. For this key problem in statistical and sequential learning theory,  we derive a variant of the Bayesian Online Change Point Detector proposed by \\cite{adams2007bayesian} which is easier to analyze than the original version while keeping its powerful message-passing algorithm. \n\tWe provide a non-asymptotic analysis of the false-alarm rate and the detection delay that matches the existing lower-bound. We further provide the first explicit high-probability control of the detection delay for such approach. Experiments on synthetic and real-world data show that this proposal compares favorably with the state-of-art change-point detection strategy, namely the Improved Generalized Likelihood Ratio (Improved GLR) while outperforming the original Bayesian Online Change Point Detection strategy.","url_abs":"https://proceedings.icml.cc/static/paper_files/icml/2020/4076-Paper.pdf","url_pdf":"https://proceedings.icml.cc/static/paper_files/icml/2020/4076-Paper.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":"restarted-bayesian-online-change-point","repo_url":"https://github.com/Ralami1859/Restarted-BOCPD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"change-point-detection","task_name":"Change Point Detection"},{"task_slug":"learning-theory","task_name":"Learning Theory"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}