{"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/maximum-mean-discrepancy-gradient-flow","title":"Maximum Mean Discrepancy Gradient Flow","arxiv_id":"1906.04370","date":"2019-06-11","proceeding":"NeurIPS 2019 12","authors":["Michael Arbel","Anna Korba","Adil Salim","Arthur Gretton"],"abstract":"We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reproducing kernel Hilbert space (RKHS), and serves as a metric on probability measures for a sufficiently rich RKHS. 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