BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Usman Khan (Tufts University) DTSTART:20210709T130000Z DTEND:20210709T140000Z DTSTAMP:20260423T021000Z UID:MPML/51 DESCRIPTION:Title: Di stributed ML: Optimal algorithms for distributed stochastic non-convex opt imization\nby Usman Khan (Tufts University) as part of Mathematics\, P hysics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nIn many emerging applications\, it is of paramount interest to learn hidden parameters fro m data. For example\, self-driving cars may use onboard cameras to identif y pedestrians\, highway lanes\, or traffic signs in various light and weat her conditions. Problems such as these can be framed as classification\, r egression\, or risk minimization in general\, at the heart of which lies s tochastic optimization and machine learning. In many practical scenarios\, distributed and decentralized learning methods are preferable as they ben efit from a divide-and-conquer approach towards data at the expense of loc al (short-range) communication. In this talk\, I will present our recent w ork that develops a novel algorithmic framework to address various aspects of decentralized stochastic first-order optimization methods for non-conv ex problems. A major focus will be to characterize regimes where decentral ized solutions outperform their centralized counterparts and lead to optim al convergence guarantees. Moreover\, I will characterize certain desirabl e attributes of decentralized methods in the context of linear speedup and networkindependent convergence rates. Throughout the talk\, I will demons trate such key aspects of the proposed methods with the help of provable t heoretical results and numerical experiments on real data.\n LOCATION:https://researchseminars.org/talk/MPML/51/ END:VEVENT END:VCALENDAR