{"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/exploring-the-wasserstein-metric-for-survival","title":"Exploring the Wasserstein metric for survival analysis","arxiv_id":null,"date":"2021-03-01","proceeding":"Proceedings of Machine Learning Research 1:1–13 2021 3","authors":["Margaux Luck*","Tristan Sylvain*","Joseph Paul Cohen","Heloise Cardinal","Andrea Lodi","Yoshua Bengio"],"abstract":"Survival analysis is a type of semi-supervised task where the target output (the survival time) is often\r\nright-censored. Utilizing this information is a challenge because it is not obvious how to correctly\r\nincorporate these censored examples into a model. We study how three categories of loss functions\r\ncan take advantage of this information: partial likelihood methods, rank methods, and our own\r\nclassification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM)\r\nestimate of the probability density to impute the labels of censored examples. The proposed method\r\npredicts the probability distribution of an event, letting us compute survival curves and expected times\r\nof survival that are easier to interpret than the rank. We also demonstrate that this approach directly\r\noptimizes the expected C-index which is the most common evaluation metric for survival models.","url_abs":"http://proceedings.mlr.press/v146/sylvain21a/sylvain21a.pdf","url_pdf":"http://proceedings.mlr.press/v146/sylvain21a/sylvain21a.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":"exploring-the-wasserstein-metric-for-survival","repo_url":"https://github.com/Museau/Survival_Pytorch_EMD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"survival-analysis","task_name":"Survival Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}