Timezone: »

 
Oral
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier · Maxime Vono · Alain Durmus · Eric Moulines

Thu Jul 22 07:00 AM -- 07:20 AM (PDT) @
in Probabilistic Methods 3 »

Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their design to handle distributed datasets has been the subject of many works. However, existing methods are not completely either reliable or computationally efficient. In this paper, we propose to fill this gap in the case where the dataset is partitioned and stored on computing nodes within a cluster under a master/slaves architecture. We derive a user-friendly centralised distributed MCMC algorithm with provable scaling in high-dimensional settings. We illustrate the relevance of the proposed methodology on both synthetic and real data experiments.

Keywords: Monte Carlo Methods

Author Information

Vincent Plassier (Polytechnique)
Maxime Vono (Lagrange Mathematics and Computing Research Center)
Alain Durmus (ENS Paris Saclay)
Eric Moulines (Ecole Polytechnique)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors