BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Isay Katsman (Yale) DTSTART:20221024T121500Z DTEND:20221024T131500Z DTSTAMP:20260423T010636Z UID:MaML/1 DESCRIPTION:Title: Rie mannian Geometry in Machine Learning\nby Isay Katsman (Yale) as part o f Mathematics and Machine Learning\n\n\nAbstract\nAlthough machine learnin g researchers have introduced a plethora of useful constructions for learn ing over Euclidean space\, numerous types of data in various applications benefit from\, if not necessitate\, a non-Euclidean treatment. In this tal k I cover the need for Riemannian geometric constructs to (1) build more p rincipled generalizations of common Euclidean operations used in geometric machine learning models as well as to (2) enable general manifold density learning in contexts that require it. Said contexts include theoretical p hysics\, robotics\, and computational biology. I will cover one of my pape rs that fits into (1) above\, namely the ICML 2020 paper “Differentiatin g through the Fréchet Mean.” I will also cover two of my papers that fi t into (2) above\, namely the NeurIPS 2020 paper “Neural Manifold ODEs ” and the NeurIPS 2021 paper “Equivariant Manifold Flows.” Finally\, I will briefly discuss directions of relevant ongoing work.\n LOCATION:https://researchseminars.org/talk/MaML/1/ END:VEVENT END:VCALENDAR