88 citations · 487 across the 78 of their papers we have counts for
9 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…
Unbiased constrained sampling with Self-Concordant Barrier Hamiltonian Monte Carlo
Maxence Noble, Valentin De Bortoli, Alain Durmus
In this paper, we propose Barrier Hamiltonian Monte Carlo (BHMC), a version of the HMC algorithm which aims at sampling from a Gibbs distribution on a manifold , en…
Variational Inference of overparameterized Bayesian Neural Networks: a theoretical and empirical study
Tom Huix, Szymon Majewski, Alain Durmus +2
This paper studies the Variational Inference (VI) used for training Bayesian Neural Networks (BNN) in the overparameterized regime, i.e., when the number of neurons tends to infini…
Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation
Alain Durmus, Eric Moulines, Alexey Naumov +1
This paper provides a finite-time analysis of linear stochastic approximation (LSA) algorithms with fixed step size, a core method in statistics and machine learning. LSA is used t…
FedPop: A Bayesian Approach for Personalised Federated Learning
Nikita Kotelevskii, Maxime Vono, Eric Moulines +1
Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction,…
On Maximum-a-Posteriori estimation with Plug & Play priors and stochastic gradient descent
Rémi Laumont, Valentin de Bortoli, Andrés Almansa +3
Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the…
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.…
Sticky nonlinear SDEs and convergence of McKean-Vlasov equations without confinement
Alain Durmus, Andreas Eberle, Arnaud Guillin +1
We develop a new approach to study the long time behaviour of solutions to nonlinear stochastic differential equations in the sense of McKean, as well as propagation of chaos for t…
Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm
Randal Douc, Alain Durmus, Aurélien Enfroy +1
The present paper focuses on the problem of sampling from a given target distribution defined on some general state space. To this end, we introduce a novel class of non-revers…