BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Gabriel Peyré (École Normale Supérieure) DTSTART:20210414T170000Z DTEND:20210414T180000Z DTSTAMP:20260423T003251Z UID:MPML/43 DESCRIPTION:Title: Sc aling Optimal Transport for High dimensional Learning\nby Gabriel Peyr é (École Normale Supérieure) as part of Mathematics\, Physics and Machi ne Learning (IST\, Lisbon)\n\n\nAbstract\nOptimal transport (OT) has recen tly gained lot of interest in machine learning. It is a natural tool to co mpare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is how ever plagued by the curse of dimensionality\, since it might require a num ber of samples which grows exponentially with the dimension. In this talk\ , I will explain how to leverage entropic regularization methods to define computationally efficient loss functions\, approximating OT with a better sample complexity.\n\nMore information and references can be found on the website of our book\n"Computational Optimal Transport"\, https://optimalt ransport.github.io/\n LOCATION:https://researchseminars.org/talk/MPML/43/ END:VEVENT END:VCALENDAR