BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250801T200042EDT-0889TCXFht@132.216.98.100 DTSTAMP:20250802T000042Z DESCRIPTION:Title: Convergence of Empirical Measures\, Mean-Field Games and Deep Learning Algorithms.\n\nAbstract: In this talk\, we first propose a new class of metrics and show that under such metrics\, the convergence of empirical measures in high dimensions is free of the curse of dimensional ity\, in contrast to Wasserstein distance. Proposed metrics are the integr al probability metrics\, where we propose criteria for test function space s. Examples include RKHS\, Barron space\, and flow-induced function spaces . One application studies the construction of Nash equilibrium for the hom ogeneous n-player game by its mean-field limit (mean-field game). Another application is to show the ability to overcome curves of dimensionality of deep learning algorithms\, for example\, in solving Mckean-Vlasov forward -backward stochastic differential equations with general distribution depe ndence. This is joint work with Jiequn Han and Jihao Long. Résumé In this talk\, we first propose a new class of metrics and show that under such me trics\, the convergence of empirical measures in high dimensions is free o f the curse of dimensionality\, in contrast to Wasserstein distance. Propo sed metrics are the integral probability metrics\, where we propose criter ia for test function spaces. Examples include RKHS\, Barron space\, and fl ow-induced function spaces. One application studies the construction of Na sh equilibrium for the homogeneous n-player game by its mean-field limit ( mean-field game). Another application is to show the ability to overcome c urves of dimensionality of deep learning algorithms\, for example\, in sol ving Mckean-Vlasov forward-backward stochastic differential equations with general distribution dependence. This is joint work with Jiequn Han and J ihao Long.\n\nZoom Lecture \n\nhttp://www.crm.umontreal.ca/cal/en/%0A%20ht tp://quantact.uqam.ca/\n DTSTART:20220916T190000Z DTEND:20220916T200000Z SUMMARY:Ruimeng Lu\, UC Santa Barbara URL:/mathstat/channels/event/ruimeng-lu-uc-santa-barba ra-341774 END:VEVENT END:VCALENDAR