488 citations · 3k across the 119 of their papers we have counts for
7 papers · 1 filter
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines +1
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because th…
f-SAEM: A fast Stochastic Approximation of the EM algorithm for nonlinear mixed effects models
Belhal Karimi, Marc Lavielle, Eric Moulines
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely us…
Unifying mirror descent and dual averaging
Anatoli Juditsky, Joon Kwon, Éric Moulines
We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging. Within the framework of this fami…
Variance reduction for Markov chains with application to MCMC
D. Belomestny, L. Iosipoi, E. Moulines +2
In this paper we propose a novel variance reduction approach for additive functionals of Markov chains based on minimization of an estimate for the asymptotic variance of these fun…
A quantitative Mc Diarmid's inequality for geometrically ergodic Markov chains
Antoine Havet, Matthieu Lerasle, Eric Moulines +1
We state and prove a quantitative version of the bounded difference inequality for geometrically ergodic Markov chains. Our proof uses the same martingale decomposition as \cite{MR…
Variance reduction for additive functional of Markov chains via martingale representations
D. Belomestny, E. Moulines, S. Samsonov
In this paper we propose an efficient variance reduction approach for additive functionals of Markov chains relying on a novel discrete time martingale representation. Our approach…
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi, Blazej Miasojedow, Eric Moulines +1
Stochastic approximation (SA) is a key method used in statistical learning. Recently, its non-asymptotic convergence analysis has been considered in many papers. However, most of t…