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02/22/2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic approximation (SA) is a classical algorithm that has had sinc...
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01/02/2023
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
This paper introduces a novel algorithm, the Perturbed Proximal Precondi...
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03/17/2022
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
Monitoring the Covid19 pandemic constitutes a critical societal stake th...
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02/11/2022
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Monitoring the evolution of the Covid19 pandemic constitutes a critical ...
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11/03/2021
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
The Expectation Maximization (EM) algorithm is the default algorithm for...
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05/25/2021
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
Incremental Expectation Maximization (EM) algorithms were introduced to ...
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12/29/2020
Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence
Fast Incremental Expectation Maximization (FIEM) is a version of the EM ...
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11/30/2020
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inf...
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11/24/2020
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
The Expectation Maximization (EM) algorithm is a key reference for infer...
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10/13/2015
Spatial Prediction Under Location Uncertainty In Cellular Networks
Coverage optimization is an important process for the operator as it is ...
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05/26/2015