BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Akiko Takeda (University of Tokyo) DTSTART:20200729T070000Z DTEND:20200729T080000Z DTSTAMP:20260422T091824Z UID:VAWebinar/6 DESCRIPTION:Title: Deterministic and Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization\nby Akiko Takeda (University of Tokyo) as pa rt of Variational Analysis and Optimisation Webinar\n\n\nAbstract\nOur wor k focuses on deterministic/stochastic gradient methods for optimizing a sm ooth non-convex loss function with a non-smooth non-convex regularizer. Re search on stochastic gradient methods is quite limited\, and until recentl y no non-asymptotic convergence results have been reported. After showing a deterministic approach\, we present simple stochastic gradient algorithm s\, for finite-sum and general stochastic optimization problems\, which ha ve superior convergence complexities compared to the current state-of-the- art. We also compare our algorithms’ performance in practice for empiric al risk minimization.\n\nThis is based on joint works with Tianxiang Liu\ , Ting Kei Pong and Michael R. Metel.\n LOCATION:https://researchseminars.org/talk/VAWebinar/6/ END:VEVENT END:VCALENDAR