Stein Self-Repulsive Dynamics: Benefits from Past Samples

Anonymous

Sep 25, 2019 ICLR 2020 Conference Blind Submission readers: everyone Show Bibtex
  • Keywords: Approximate Inference, Markov Chain Monte Carlo, Stein Variational Gradient Descent
  • TL;DR: We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions.
  • Abstract: We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions. Our idea is to introduce Stein variational gradient as a repulsive force to push the samples of Langevin dynamics away from the past trajectories. This simple idea allows us to significantly decrease the auto-correlation in Langevin dynamics and hence increase the effective sample size. Importantly, as we establish in our theoretical analysis, the asymptotic stationary distribution remains correct even with the addition of the repulsive force, thanks to the special properties of the Stein variational gradient. We perform extensive empirical studies of our new algorithm, showing that our method yields much higher sample efficiency and better uncertainty estimation than vanilla Langevin dynamics.
  • Code: https://www.dropbox.com/sh/xa6ihm45g181tmf/AAB6fysesxLJ0XfzRYHQlF3xa?dl=0
0 Replies

Loading