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SUMMARY:Michael I. Jordan (UC Berkeley)
DTSTART:20200611T190000Z
DTEND:20200611T203000Z
DTSTAMP:20260423T003245Z
UID:IASML/5
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/IASML/5/">On
  Langevin Dynamics in Machine Learning</a>\nby Michael I. Jordan (UC Berke
 ley) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\nAb
 stract\nLangevin diffusions are continuous-time stochastic processes that 
 are based on the gradient of a potential function. As such they have many 
 connections---some known and many still to be explored---to gradient-based
  machine learning. I'll discuss several recent results in this vein: (1) t
 he use of Langevin-based algorithms in bandit problems\; (2) the accelerat
 ion of Langevin diffusions\; (3) how to use Langevin Monte Carlo without m
 aking smoothness assumptions. I'll present these results in the context of
  a general argument about the virtues of continuous-time perspectives in t
 he analysis of discrete-time optimization and Monte Carlo algorithms.\n
LOCATION:https://researchseminars.org/talk/IASML/5/
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