G. Fort

Berna Sayraç, Sana Ben Jemaa, Gersende Fort, 2014, 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt).

Convergence of adaptive and interacting Markov chain Monte Carlo algorithms

G. Fort, E. Moulines, P. Priouret, 2011, 1203.3036.

A central limit theorem for adaptive and interacting Markov chains

P. Priouret, É. Moulines, G. Fort, 2011, 1107.2574.

Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields

Gersende Fort, Florence Forbes, G. Fort, 2007, IEEE Transactions on Image Processing.

Online Expectation Maximization algorithm to solve the SLAM problem

É. Moulines, G. Fort, S. Le Corff, 2011, 2011 IEEE Statistical Signal Processing Workshop (SSP).

Adaptive MCMC with online relabeling

Olivier Capp'e, R. Bardenet, G. Fort, 2012, 1210.2601.

Adaptive MCMC with online relabeling Long version

G. Fort, O. Capp, 2013 .

Adaptive MCMC with online relabeling (Extended version)

B. Kégl, O. Cappé, R. Bardenet, 2012 .

Forgetting the initial distribution for Hidden Markov Models

Gersende Fort, Eric Moulines, Pierre Priouret, 2007 .

Bayesian model comparison in cosmology with Population Monte Carlo

O. Cappé, C. Robert, J. Cardoso, 2009, 0912.1614.

The ODE method for stability of skip-free Markov chains with applications to MCMC

Gersende Fort, Eric Moulines, Pierre Priouret, 2006, math/0607800.

On Perturbed Proximal Gradient Algorithms

Gersende Fort, Eric Moulines, Yves F. Atchadé, 2014, J. Mach. Learn. Res..

On stochastic proximal gradient algorithms

É. Moulines, G. Fort, Y. Atchadé, 2014 .

Sampling Multimodal densities on large dimensional spaces

Gersende Fort, G. Fort, 2014 .

Efficiency of the Wang–Landau Algorithm: A Simple Test Case

B. Jourdain, T. Lelièvre, G. Fort, 2013, 1310.6550.

Réduction de dimension dans les modèles linéaires généralisés : Application à la classification supervisée de données issues des biopuces

G. Fort, S. Lambert-Lacroix, Julia Peyre, 2005 .

Quantification d'incertitude pour l'Approximation Stochastique

E. Gobet, G. Fort, S. Crépey, 2019 .

Credibility interval Design for Covid19 Reproduction Number from Nonsmooth Langevin-type Monte Carlo sampling

G. Fort, P. Abry, N. Pustelnik, 2022, 2022 30th European Signal Processing Conference (EUSIPCO).

Estimations pour les modeles de Markov caches et approximations particulaires Applicationla cartographie etla localisation simultanees

Gersende Fort, Eric Moulines, M. Gilles Pag, 2012 .

Subgeometric rates of convergence of f-ergodic strong Markov processes

R. Douc, A. Guillin, G. Fort, 2006, math/0605791.

Forgetting of the initial distribution for Hidden Markov Models 1

P. Priouret, R. Douc, É. Moulines, 2013 .

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

B. Jourdain, T. Lelièvre, G. Fort, 2018, Journal of Statistical Physics.

An online Minorization-Maximization algorithm

O. Cappé, G. Fort, F. Forbes, 2022 .

Stochastic proximal-gradient algorithms for penalized mixed models

A. Samson, G. Fort, E. Ollier, 2018, Statistics and Computing.

Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization

É. Moulines, G. Fort, 2023, 2301.00631.

Quantitative Convergence Rates for Subgeometric Markov Chains

C. Andrieu, M. Vihola, G. Fort, 2013, Journal of Applied Probability.

Stochastic Fista Algorithms: So Fast ?

Gersende Fort, Eric Moulines, Laurent Risser, 2018, 2018 IEEE Statistical Signal Processing Workshop (SSP).

Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers

G. Fort, P. Abry, N. Pustelnik, 2022, IEEE Transactions on Signal Processing.

Temporal evolution of the Covid19 pandemic reproduction number: Estimations from proximal optimization to Monte Carlo sampling

G. Fort, P. Abry, N. Pustelnik, 2022, 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Classification using partial least squares with penalized logistic regression

Gersende Fort, Sophie Lambert-Lacroix, G. Fort, 2005, Bioinform..

Adaptive and Interacting Monte Carlo methods for Bayesian analysis

G. Fort, 2012 .

CosmoPMC: Cosmology Population Monte Carlo

Gersende Fort, Simon Prunet, Christian P. Robert, 2011, 1101.0950.

Forgetting the initial distribution for Hidden Markov Models

P. Priouret, R. Douc, É. Moulines, 2007, math/0703836.

State-dependent Foster–Lyapunov criteria for subgeometric convergence of Markov chains

Gersende Fort, Stephen B. Connor, G. Fort, 2009, 0901.2453.

Stochastic proximal-gradient algorithms for penalized mixed models

Gersende Fort, Edouard Ollier, Adeline Samson, 2017, Statistics and Computing.

Spatial Prediction Under Location Uncertainty in Cellular Networks

Berna Sayraç, Sana Ben Jemaa, Gersende Fort, 2015, IEEE Transactions on Wireless Communications.

Performance of a Distributed Stochastic Approximation Algorithm

Pascal Bianchi, Gersende Fort, Walid Hachem, 2012, IEEE Transactions on Information Theory.

Fixed Rank Kriging for Cellular Coverage Analysis

Berna Sayraç, Sana Ben Jemaa, Gersende Fort, 2015, IEEE Transactions on Vehicular Technology.

Success and Failure of Adaptation-Diffusion Algorithms With Decaying Step Size in Multiagent Networks

Pascal Bianchi, Gersende Fort, Gemma Morral, 2017, IEEE Transactions on Signal Processing.

Uncertainty Quantification for Stochastic Approximation Limits Using Chaos Expansion

Gersende Fort, Emmanuel Gobet, Stéphane Crépey, 2020, SIAM/ASA J. Uncertain. Quantification.

Subgeometric rates of convergence in Wasserstein distance for Markov chains

É. Moulines, Alain Durmus, G. Fort, 2014, 1402.4577.

Quantitative Convergence Rates for Subgeometric Markov Chains

Gersende Fort, Christophe Andrieu, Matti Vihola, 2013, J. Appl. Probab..

Limit theorems for some adaptive MCMC algorithms with subgeometric kernels

G. Fort, Y. Atchadé, 2008, 0807.2952.

MCMC design-based non-parametric regression for rare event. Application to nested risk computations

Gersende Fort, Eric Moulines, Emmanuel Gobet, 2017, Monte Carlo Methods Appl..

Explicit control of subgeometric ergodicity

Gersende Fort, Christophe Andrieu, C. Andrieu, 2005 .

Stochastic variable metric proximal gradient with variance reduction for non-convex composite optimization

É. Moulines, G. Fort, 2023, Statistics and Computing.

Central Limit Theorems for Stochastic Approximation with controlled Markov chain dynamics

Gersende Fort, G. Fort, 2013, 1309.3116.

Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning

É. Moulines, Aymeric Dieuleveut, Hoi-To Wai, 2023, IEEE Transactions on Signal Processing.

A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm

Gersende Fort, Eric Moulines, Hoi-To Wai, 2020, NeurIPS.

Federated-EM with heterogeneity mitigation and variance reduction

É. Moulines, Aymeric Dieuleveut, G. Fort, 2021, NeurIPS.

Convergence of Markovian Stochastic Approximation with Discontinuous Dynamics

G. Fort, E. Moulines, A. Schreck, 2014, SIAM J. Control. Optim..

Estimation of cosmological parameters using adaptive importance sampling

Gersende Fort, Simon Prunet, Darren Wraith, 2009, 0903.0837.

Adaptive Equi-Energy Sampler: Convergence and Illustration

Gersende Fort, Eric Moulines, Amandine Schreck, 2012, TOMC.

Classification using partial least squares with penalized logistic regression

G. Fort, S. Lambert-Lacroix, 2005, Bioinform..

New conditions for subgeometric rates of convergence in the Wasserstein distance for Markov chains

É. Moulines, Alain Durmus, G. Fort, 2014 .

Central Limit Theorems for stochastic approximation algorithms

Gersende Fort, G. Fort, 2012 .

Federated Expectation Maximization with heterogeneity mitigation and variance reduction pdf

Gersende Fort, Eric Moulines, Aymeric Dieuleveut, 2021, ArXiv.

Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence pdf

Gersende Fort, E. Moulines, P. Gach, 2020, Statistics and Computing.

A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm pdf

Gersende Fort, Eric Moulines, Hoi-To Wai, 2020, ArXiv.

Convergence of a Particle-Based Approximation of the Block Online Expectation Maximization Algorithm

Gersende Fort, Sylvain Le Corff, G. Fort, 2011, TOMC.

Modelling and Inference of Complex and Structured Stochastic Systems

Charles Bouveyron, Gersende Fort, Chibiao Chen, 2006 .

On the geometric ergodicity of hybrid samplers

G. Fort, Eric Moulines, Gareth O. Roberts, 2003, Journal of Applied Probability.

The Perturbed Prox-Preconditioned Spider Algorithm: Non-Asymptotic Convergence Bounds

Gersende Fort, Eric Moulines, É. Moulines, 2021, 2021 IEEE Statistical Signal Processing Workshop (SSP).

Coverage mapping using spatial interpolation with field measurements

Berna Sayraç, Sana Ben Jemaa, Gersende Fort, 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).