90 citations · 403 across the 37 of their papers we have counts for
6 papers · 1 filter
Federated Majorize-Minimization: Beyond Parameter Aggregation
Aymeric Dieuleveut, Gersende Fort, Mahmoud Hegazy +1
This paper proposes a unified approach for designing stochastic optimization algorithms that robustly scale to the federated learning setting. Our work studies a class of Majorize-…
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
Gersende Fort, Eric Moulines
This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization. It is a…
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
Gersende Fort, Barbara Pascal, Patrice Abry +1
Monitoring the Covid19 pandemic constitutes a critical societal stake that received considerable research efforts. The intensity of the pandemic on a given territory is efficiently…
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
Gersende Fort, Eric Moulines
Incremental Expectation Maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each itera…
Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence
Gersende Fort, P. Gach, E. Moulines
Fast Incremental Expectation Maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms i…
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…