28 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Laboratoire de Mathématiques Blaise PascalFR35 papers
- Laboratoire de Mathématiques et Modélisation d'ÉvryFR35 papers
- LAM FoundationUS14 papers
- Centre de Mathématiques Appliquées de l'École polytechniqueFR11 papers
- Centre National de la Recherche ScientifiqueFR11 papers
- École PolytechniqueFR10 papers
- Université d'Évry Val-d'EssonneFR9 papers
- National Research University Higher School of EconomicsRU6 papers
- Université Paris-SaclayFR6 papers
- École Nationale Supérieure d’Informatique pour l’Industrie et l’EntrepriseFR5 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR5 papers
- Chimie de la Matière Condensée de ParisFR4 papers
5 papers · 1 filter
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…
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 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…
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…
High-dimensional robust regression and outliers detection with SLOPE
Alain Virouleau, Agathe Guilloux, Stéphane Gaïffas +1
The problems of outliers detection and robust regression in a high-dimensional setting are fundamental in statistics, and have numerous applications. Following a recent set of work…