Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Sampling through Exploration Exploitation in Markov Chain Monte Carlo

Institut des Hautes Etudes Scientifiques (IHES) via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore an innovative Markov chain Monte Carlo algorithm called Ex2MCMC in this 35-minute conference talk by Eric Moulines from Ecole Polytechnique. Delve into the development of this massively parallelizable and computationally efficient method that combines multiple global proposals with mobile moves. Learn about the algorithm's V-uniform geometric ergodicity under realistic conditions and understand the explicit bounds on mixing rates that demonstrate improvements due to multiple global moves. Discover how Ex2MCMC allows for fine-tuning of exploitation (local moves) and exploration (global moves) through a novel approach to proposing dependent global moves. Examine the adaptive scheme, FlEx2MCMC, which learns the distribution of global trains using normalizing flows. Gain insights into the efficiency of Ex2MCMC and its adaptive versions through various classical sampling benchmarks, and see how these algorithms enhance the quality of sampling GANs as energy-based models.

Syllabus

Eric Moulines - Sampling through Exploration Exploitation

Taught by

Institut des Hautes Etudes Scientifiques (IHES)

Reviews

Start your review of Sampling through Exploration Exploitation in Markov Chain Monte Carlo

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.