488 citations · 3k across the 119 of their papers we have counts for
8 papers · 1 filter
Nonreversible MCMC from conditional invertible transforms: a complete recipe with convergence guarantees
Achille Thin, Nikita Kotelevskii, Christophe Andrieu +3
Markov Chain Monte Carlo (MCMC) is a class of algorithms to sample complex and high-dimensional probability distributions. The Metropolis-Hastings (MH) algorithm, the workhorse of…
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
Gersende Fort, Eric Moulines, Hoi-To Wai
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations. This pap…
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
Gersende Fort, Eric Moulines, Hoi-To Wai
The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale lear…
Variance reduction for dependent sequences with applications to Stochastic Gradient MCMC
D. Belomestny, L. Iosipoi, E. Moulines +2
In this paper we propose a novel and practical variance reduction approach for additive functionals of dependent sequences. Our approach combines the use of control variates with t…
Convergence Analysis of Riemannian Stochastic Approximation Schemes
Alain Durmus, Pablo Jiménez, Éric Moulines +2
This paper analyzes the convergence for a large class of Riemannian stochastic approximation (SA) schemes, which aim at tackling stochastic optimization problems. In particular, th…
MetFlow: A New Efficient Method for Bridging the Gap between Markov Chain Monte Carlo and Variational Inference
Achille Thin, Nikita Kotelevskii, Jean-Stanislas Denain +4
In this contribution, we propose a new computationally efficient method to combine Variational Inference (VI) with Markov Chain Monte Carlo (MCMC). This approach can be used with g…
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Maxim Kaledin, Eric Moulines, Alexey Naumov +2
Linear two-timescale stochastic approximation (SA) scheme is an important class of algorithms which has become popular in reinforcement learning (RL), particularly for the policy e…
On Last-Layer Algorithms for Classification: Decoupling Representation from Uncertainty Estimation
Nicolas Brosse, Carlos Riquelme, Alice Martin +2
Uncertainty quantification for deep learning is a challenging open problem. Bayesian statistics offer a mathematically grounded framework to reason about uncertainties; however, ap…