BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Valentin De Bortoli (Center for Sciences of Data\, ENS Ulm\, Paris ) DTSTART:20230316T170000Z DTEND:20230316T180000Z DTSTAMP:20260423T003245Z UID:MPML/99 DESCRIPTION:Title: Di ffusion models\, theory and methodology\nby Valentin De Bortoli (Cente r for Sciences of Data\, ENS Ulm\, Paris) as part of Mathematics\, Physics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nGenerative modeling is the task of drawing new samples from an underlying distribution known onl y via an empirical measure. There exists a myriad of models to tackle this problem with applications in image and speech processing\, medical imagin g\, forecasting and protein modeling to cite a few. Among these methods di ffusion models are a new powerful class of generative models that exhibit remarkable empirical performance. They consist of a ``noising'' stage\, wh ereby a diffusion is used to gradually add Gaussian noise to data\, and a generative model\, which entails a ``denoising'' process defined by approx imating the time-reversal of the diffusion. In this talk we discuss three aspects of diffusion models. First\, we will dive into the methodology beh ind diffusion models. Second\, we will present some of their theoretical g uarantees with an emphasis on their behavior under the so-called manifold hypothesis. Such theoretical guarantees are non-vacuous and provide insigh t on the empirical behavior of these models. Finally\, I will present an e xtension of diffusion models to the Optimal Transport setting and introduc e Diffusion Schrodinger Bridges.\n LOCATION:https://researchseminars.org/talk/MPML/99/ END:VEVENT END:VCALENDAR