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Found 12 papers, 1 papers with code
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
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 to deal with a large amount of data observed with uncertainties.
Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization
This paper introduces a novel algorithm, the Perturbed Proximal Preconditioned SPIDER algorithm (3P-SPIDER), designed to solve finite sum non-convex composite optimization.
Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers
The originality of the devised algorithms stems from combining a Langevin Monte Carlo sampling scheme with Proximal operators.
Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling
Yet, the assessment of the pandemic intensity within the pandemic period remains a challenging task because of the limited quality of data made available by public health authorities (missing data, outliers and pseudoseasonalities, notably), that calls for cumbersome and ad-hoc preprocessing (denoising) prior to estimation.
Federated-EM with heterogeneity mitigation and variance reduction
The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models.
Federated Expectation Maximization with heterogeneity mitigation and variance reduction
The Expectation Maximization (EM) algorithm is the default algorithm for inference in latent variable models.
The perturbed prox-preconditioned spider algorithm: non-asymptotic convergence bounds
A novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER) is introduced.
The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning
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 iteration.
Fast Incremental Expectation Maximization for finite-sum optimization: nonasymptotic convergence
Second, for the $n^{2/3}$-rate, the numerical illustrations show that thanks to an optimized choice of the step size and of the bounds in terms of quantities characterizing the optimization problem at hand, our results desig a less conservative choice of the step size and provide a better control of the convergence in expectation.
A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
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 learning setting.
