| Classification using partial least squares with penalized logistic regression G Fort, S Lambert-Lacroix Bioinformatics 21 (7), 1104-1111, 2005 | 253 | 2005 |
| Convergence of the Monte Carlo expectation maximization for curved exponential families G Fort, E Moulines The Annals of Statistics 31 (4), 1220-1259, 2003 | 184 | 2003 |
| Practical drift conditions for subgeometric rates of convergence R Douc, G Fort, E Moulines, P Soulier | 183 | 2004 |
| Subgeometric rates of convergence of f-ergodic strong Markov processes R Douc, G Fort, A Guillin Stochastic processes and their applications 119 (3), 897-923, 2009 | 179 | 2009 |
| Performance of a distributed stochastic approximation algorithm P Bianchi, G Fort, W Hachem IEEE Transactions on Information Theory 59 (11), 7405-7418, 2013 | 122 | 2013 |
| Convergence of adaptive and interacting Markov chain Monte Carlo algorithms G Fort, E Moulines, P Priouret | 106 | 2011 |
| Estimation of cosmological parameters using adaptive importance sampling D Wraith, M Kilbinger, K Benabed, O Cappe, JF Cardoso, G Fort, S Prunet, ... Physical Review D 80 (2), 023507, 2009 | 97 | 2009 |
| On perturbed proximal gradient algorithms YF Atchadé, G Fort, E Moulines The Journal of Machine Learning Research 18 (1), 310-342, 2017 | 96 | 2017 |
| Adaptive Markov chain Monte Carlo: theory and methods Y Atchade, G Fort, E Moulines, P Priouret Bayesian time series models 1, 2011 | 84 | 2011 |
| Subgeometric ergodicity of strong Markov processes G Fort, GO Roberts | 81 | 2005 |
| Polynomial ergodicity of Markov transition kernels G Fort, E Moulines Stochastic Processes and their Applications 103 (1), 57-99, 2003 | 78 | 2003 |
| Bayesian model comparison in cosmology with Population Monte Carlo M Kilbinger, D Wraith, CP Robert, K Benabed, O Cappé, JF Cardoso, ... Monthly Notices of the Royal Astronomical Society 405 (4), 2381-2390, 2010 | 69 | 2010 |
| Limit theorems for some adaptive MCMC algorithms with subgeometric kernels Y Atchadé, G Fort | 67 | 2010 |
| V-subgeometric ergodicity for a Hastings–Metropolis algorithm G Fort, E Moulines Statistics & probability letters 49 (4), 401-410, 2000 | 65 | 2000 |
| Forgetting the initial distribution for hidden Markov models R Douc, G Fort, E Moulines, P Priouret Stochastic processes and their applications 119 (4), 1235-1256, 2009 | 64 | 2009 |
| On stochastic proximal gradient algorithms YF Atchade, G Fort, E Moulines arXiv preprint arXiv:1402.2365 23, 2014 | 54 | 2014 |
| On the geometric ergodicity of hybrid samplers G Fort, E Moulines, GO Roberts, JS Rosenthal Journal of Applied Probability 40 (1), 123-146, 2003 | 54 | 2003 |
| Fixed rank kriging for cellular coverage analysis H Braham, SB Jemaa, G Fort, E Moulines, B Sayrac IEEE Transactions on Vehicular Technology 66 (5), 4212-4222, 2016 | 49 | 2016 |
| Combining Monte Carlo and mean-field-like methods for inference in hidden Markov random fields F Forbes, G Fort IEEE transactions on image processing 16 (3), 824-837, 2007 | 49 | 2007 |
| Convergence of the Wang-Landau algorithm G Fort, B Jourdain, E Kuhn, T Lelièvre, G Stoltz Mathematics of Computation 84 (295), 2297-2327, 2015 | 43 | 2015 |
Eric MoulinesProfesseur, Ecole Polytechnique, Membre de l'Académie des SciencesVerified email at polytechnique.edu
