Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows
Authors: Chris Cannella, Mohammadreza Soltani, Vahid Tarokh
Venue: ICLR 2021 Poster
Keywords: Conditional Sampling, Normalizing Flows, Markov Chain Monte Carlo, Missing Data Inference
Abstract: We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through experimental tests applying normalizing flows to missing data tasks for a variety of data sets, we demonstrate the efficacy of PL-MCMC for conditional sampling from normalizing flows.