183 citations · 724 across the 42 of their papers we have counts for
4 papers · 1 filter
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…
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Particle-based, rapid incremental smoother meets particle Gibbs
Gabriel Cardoso, Eric Moulines, Jimmy Olsson
The particle-based, rapid incremental smoother (PARIS) is a sequential Monte Carlo technique allowing for efficient online approximation of expectations of additive functionals und…
On the geometric convergence for MALA under verifiable conditions
Alain Durmus, Éric Moulines
While the Metropolis Adjusted Langevin Algorithm (MALA) is a popular and widely used Markov chain Monte Carlo method, very few papers derive conditions that ensure its convergence.…