BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Andrea L. Bertozzi (University of California Los Angeles) DTSTART:20220505T160000Z DTEND:20220505T170000Z DTSTAMP:20260423T003256Z UID:MPML/73 DESCRIPTION:Title: Gr aph based models in semi-supervised and unsupervised learning\nby Andr ea L. Bertozzi (University of California Los Angeles) as part of Mathemati cs\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nSimilarity  graphs provide a structure for analyzing high dimensional data. These undirected weighted graphs provide structure for identifying inheren t clusters in datasets and many methods exist to sort through such data bu ilding on the graph laplacian matrix.  One way to think about such proble ms is in terms of penalized cut problems.  These can be expressed in term s of the graph total variation which has a well-known analogue in Euclidea n space.  We show how to use ideas from geometric methods for PDEs to dev elop efficient and high performing methods for semi-supervised and unsuper vised learning.  These methods also extend to active learning and to modu larity optimization for community detection on networks.\n LOCATION:https://researchseminars.org/talk/MPML/73/ END:VEVENT END:VCALENDAR