2 citations · 4 across the 2 of their papers we have counts for
2 papers
stat.ML2022★ 2 cited
Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms
Vincent Plassier, Alain Durmus, Eric Moulines
This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations o…
stat.ME2021★ 2 cited
DG-LMC: A Turn-key and Scalable Synchronous Distributed MCMC Algorithm via Langevin Monte Carlo within Gibbs
Vincent Plassier, Maxime Vono, Alain Durmus +1
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 Marko…