Theoretical Machine Learning Seminar

On Langevin Dynamics in Machine Learning

Langevin 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) the use of Langevin-based algorithms in bandit problems; (2) the acceleration of Langevin diffusions; (3) how to use Langevin Monte Carlo without making smoothness assumptions. I'll present these results in the context of a general argument about the virtues of continuous-time perspectives in the analyis of discrete-time optimization and Monte Carlo algorithms.

Date & Time

June 11, 2020 | 3:00pm – 4:30pm

Location

Remote Access Only - see link below

Speakers

Michael I. Jordan

Speaker Affiliation

University of California, Berkeley

Notes

We welcome broad participation in our seminar series. To receive login details, interested participants will need to fill out a registration form accessible from the link below.  Upcoming seminars in this series can be found here.

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