90 citations · 403 across the 37 of their papers we have counts for
45 papers
Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts
TrungKhang Tran, TrungTin Nguyen, Gersende Fort +5
Processing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repe…
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 Approximation Beyond Gradient for Signal Processing and Machine Learning
Aymeric Dieuleveut, Gersende Fort, Eric Moulines +1
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity…
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…
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Patrice Abry, Gersende Fort, Barbara Pascal +1
Monitoring the evolution of the Covid19 pandemic constitutes a critical step in sanitary policy design. Yet, the assessment of the pandemic intensity within the pandemic period rem…