G. Fort, F. Forbes and H.-D. Nguyen. Sequential Sample Average Majorization-Minimization. Accepted for publication in Statistics and Computing, November 2025.
G. Fort and M. Pereyra. Monte Carlo. For publication on the « Histoire du Traitement du Signal » webpage of the GRETSI association et HAL-05166306v1, June 2025.
J. Chevallier and G. Fort. Sampling Nonsmooth Log-Concave Densities : A Comparative Study of Primal-Dual Based Proposal Distributions. ICASSP 2025 HAL–04824190v1.
P. Abry, J. Chevallier, G. Fort and B. Pascal. Hierarchical Bayesian Estimation of COVID-19 Reproduction Number. ICASSP 2025, HAL-04695138v2.
P. Abry, J. Chevallier, G. Fort and B. Pascal. Pandemic Intensity Estimation From Stochastic Approximation-Based Algorithms. Accepted to CAMSAP 2023, HAL-04174245v1.
A. Dieuleveut, G. Fort, E. Moulines and H-T. Wai. Stochastic Approximation beyond Gradient for Signal Processing and Machine Learning. HAL-03979922. IEEE Trans Signal Processing; 71:3117-3148, 2023.
P. Abry, G. Fort, B. Pascal and N. Pustelnik. Credibility intervals for the reproduction number of the Covid-19 pandemic using Proximal Lanvevin samplers. HAL-03902144 Accepted for publication in EUSIPCO 2023. Slides and Video, by B. Pascal
G. Fort and E. Moulines. Stochastic Variable Metric Proximal Gradient with variance reduction for non-convex composite optimization. Statistics and Computing, Volume 33, Article 65, 30 pags. HAL-03781216
G. Fort, B. Pascal, P. Abry and N. Pustelnik. Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo Samplers.. HAL-03611079. IEEE Trans. Signal Processing, 71:888-900, 2023.
P. Abry, G. Fort, B. Pascal and N. Pustelnik. Estimation et Intervalles de crédibilité pour le taux de reproduction de la Covid19 par échantillonnage Monte Carlo Langevin Proximal. HAL-03611891. Accepted for publication in the GRETSI 2022 proceedings.
H. Artigas, B. Pascal, G. Fort, P. Abry and N. Pustelnik. Credibility Interval Design for Covid19 Reproduction Number from nonsmooth Langevin-type Monte Carlo sampling. HAL-03371837. 2022 30th European Signal Processing Conference (EUSIPCO), Belgrade, Serbia, 2022, pp. 2196-2200.
P. Abry, G. Fort, B. Pascal and N. Pustelnik. Temporal Evolution of the Covid19 pandemic reproduction number: Estimations from Proximal optimization to Monte Carlo sampling. HAL-03565440. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 167-170
H. Duy Nguyen, F. Forbes, G. Fort and O. Cappé. An online Minorization-Maximization algorithm. HAL-03542180. Accepted for publication in IFCS 2022 proceedings.
A. Dieuleveut, G. Fort, E. Moulines, G. Robin. Federated Expectation Maximization with heterogeneity mitigation and variance reduction, May 2021. Accepted for publication in the Proceedings of the 35-th Conference NeurIPS, September 2021.
[Teaching materials] G. Fort, M. Lerasle and E. Moulines. Statistique et Introduction à l’Apprentissage statistique – Polycopié (also here), Ecole Polytechnique.
G. Fort, E. Moulines and P. Gach. The Fast Incremental Expectation Maximization for finite-sum optimization: asymptotic convergence. with a Supplementary material. Matlab codes on Github. Accepted to Statistics and Computing, May 2021.
G. Fort and E. Moulines. The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning. March 2021. Accepted to IEEE Statistical Signal Processing Workshop (SSP 2021)
G. Fort and E. Moulines. The Perturbed Prox-Preconditioned SPIDER algorithm: non-asymptotic convergence bounds. March 2021. Accepted to IEEE Statistical Signal Processing Workshop (SSP 2021).
G. Fort, E. Moulines, H.-T. Wai. Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization, ICASSP 2021 – 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP):3135–3139. With supplementary material. Matlab codes on Github.
G. Fort, E. Moulines, H.T. Wai. A Stochastic Path Integrated Differential Estimator Expectation Maximization Algorithm. In Conference Proceedings NeurIPS, accepted in September 2020. (see also here) [poster, see here].
S. Crepey, G. Fort, E. Gobet and U. Stazhynski. Uncertainty quantification for Stochastic Approximation limits using Chaos Expansion. Nov17, HAL-01629952. SIAM-ASA Journal of Uncertainty Quantification, 8(3):1061-1089, 2020.
D. Barrera, S. Crepey, B. Diallo, G. Fort, E. Gobet and V. Stazhinksi. Stochastic Approximation Schemes for Economic Capital and Risk Margin Computations, ESAIM Proc (CEMRACS 2017), 65:182–218, 2019.
G. Fort, E. Ollier and A. Leclerc-Samson. Stochastic Proximal Gradient Algorithms for Penalized Mixed Models. arXiv:1704.08891. Statistics and Computing, 29(2):231-253, 2019.
S. Crepey, G. Fort, E. Gobet and U. Stazhynski. Quantification d’Incertitude pour l’Approximation Stochastique. GRETSI, August 2019.
G. Fort, B. Jourdain, T. Lelièvre and G. Stoltz. Convergence and Efficiency of Adaptive Importance Sampling techniques with partial biasing. Journal of Statistical Physics, 171(2):220-268, 2018.
G. Fort, L. Risser, Y. Atchadé and E. Moulines. Stochastic FISTA algorithms: so fast ? . Workshop in Statistical Signal Processing (SSP), June 2018.
G. Fort, E. Gobet and E. Moulines. MCMC design-based non-parametric regression for rare event. Application to nested risk computation. Monte Carlo Methods and Applications, 23(1):21–42, 2017.
G. Morral, P. Bianchi and G. Fort. Success and Failure of Adaptation-Diffusion Algorithms for Consensus in Multi-Agent Networks. IEEE Trans. Signal Processing, 65(11):2798-2813, 2017.
Y. Atchadé, G. Fort and E. Moulines. On perturbed proximal gradient algorithms, Submitted in February 2014 under the title « On stochastic proximal gradient algorithms ». arXiv:1402:2365 math.ST. JMLR, 18(10):1-33, 2017.
G. Fort, B. Jourdain, T. Lelièvre and G. Stoltz. Self-Healing Umbrella Sampling: convergence and efficiency. arXiv math.PR 1410.2109, submitted in October 2014. Revised in April 2015, Accepted Nov 15. Statistics and Computing, 27(1):147-168, 2017
G. Fort, L. Risser, E. Moulines, E. Ollier and A. Leclerc-Samson. Algorithmes Gradient-Proximaux stochastiques. GRETSI, September 2017.
H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B. Sayrac. Spatial prediction under location uncertainty in cellular networks, arXiv:1510:03638, IEEE Trans. Wireless Communications, 15(11):7633-7643, 2016.
H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B. Sayrac. Fixed Rank Kriging for Cellular Coverage Analysis. ArXiv:1505:07062, IEEE Trans. Vehicular Technology, 66(5):4212-4222, 2016.
A. Schreck, G. Fort, E. Moulines and M. Vihola Convergence of Markovian Stochastic Approximation with discontinuous dynamic . arXiv math.ST 1403.6803, submitted in March 2014. SIAM J. Control Optim., 54(2):866-893, 2016
A. Schreck, G. Fort, S. Le Corff and E. Moulines. A shrinkage-thresholding Metropolis adjusted Langevin algorithm for Bayesian variable selection. arXiv math.ST 1312.5658. IEEE J. of Selected Topics in Signal Processing, 10(2):366-375, 2016.
A. Durmus, G. Fort and E. Moulines. Subgeometric rates of convergence rates in Wasserstein distance for Markov chains. arXiv:1402.4577math.PR. Ann. inst. Henri Poincaré, 52(4):1799-1822, 2016.
G. Fort. Central Limit Theorems for Stochastic Approximation with Controlled Markov Chain Dynamics. EsaimPS, 19:60-80, 2015. arXiv math.PR 1309.311C.
Andrieu, G. Fort and M. Vihola. Quantitative convergence rates for sub-geometric Markov chains. Advances in Applied Probability, 52(2):391-404, 2015. arXiv math.PR 1309.0622
G. Fort, B. Jourdain, E. Kuhn, T. Lelièvre and G. Stoltz. Convergence of the Wang-Landau algorithm. Math. Comp., 84:2297-2327, 2015. arXiv:1207.6880 [math.PR]
R. Bardenet, O. Cappé, G. Fort and B. Kegl. Adaptive MCMC with Online Relabeling. (accepted for publication in 2013) Bernoulli, 21(3):1304-1340, 2015. arXiv:1210.2601 [stat.CO]
G. Fort, B. Jourdain, E. Kuhn, T. Lelièvre and G. Stoltz. Efficiency of the Wang-Landau algorithm. App. Math. Res. Express, 2914(2):275-311, 2014. arXiv:1310.6550.
G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A Central Limit Theorem for Adaptive and Interacting Markov Chains.arXiv:1107.2574 Supplement paper Bernoulli 20(2):457-485, 2014.
G. Morral, P. Bianchi and G. Fort. Success and Failure of Adaptation-Diffusion Algorithms for Consensus in Multiagent Networks. Proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), December 2014.
H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B. Sayrac. Coverage Mapping Using Spatial Interpolation With Field Measurements. Accepted for presentation and publication in the proceedings to : IEEE PIMRC – Mobile and Wireless Networks 2014, September 2014.
H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B. Sayrac. Low complexity Spatial Interpolation For Cellular Coverage Analysis. Accepted for presentation and publications in the proceedings to : WiOpt 2014, May 2014.
P. Bianchi, G. Fort and W. Hachem. Performance of a Distributed Stochastic Approximation Algorithm, IEEE Trans. on Information Theory, 59(11):7405-7418, 2013.
S. Le Corff and G. Fort. Online Expectation Maximization-based algorithms for inference in Hidden Markov Models. Electronic Journal of Statistics, 7:763-792, 2013. arXiv math.ST 1108-3968. Supplement paper, math.ST 1108-4130.
A. Schreck, G. Fort and E. Moulines. Adaptive Equi-energy sampler : convergence and illustration. ACM Transactions on Modeling and Computer Simulation (TOMACS), 23(1):Article 5 – 27 pages, 2013.
S. Le Corff and G. Fort. Convergence of a particle-based approximation of the Block online Expectation Maximization algorithm, ACM Transactions on Modeling and Computer Simulation (TOMACS) 23(1):Article2 – 22 pages, 2013.
G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Approximation stochastique distribuée : le coût de la non bistochasticit. GRETSI, September 2013.
G. Fort, E. Moulines, P. Priouret and P. Vandekerkhove. A simple variance inequality for U-statistics of a Markov chain with Applications. Statistics & Probability Letters 82(6):1193-1201, 2012.
G. Fort, E. Moulines and P. Priouret. Convergence of adaptive and interacting Markov chain Monte Carlo algorithms. Ann. Statist. 39(6):3262-3289, 2012. [Supplementary material],
Y. Atchadé and G. Fort. Limit theorems for some adaptive MCMC algorithms with subgeometric kernels, part II. Bernoulli 18(3):975-1001, 2012.
G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Distributed Stochastic Approximation: The Price of Non-double Stochasticity. ASILOMAR November 2012.
R. Bardenet, O. Cappé, G. Fort and B. Kegl. Adaptive Metropolis with online relabeling. (Supplementary paper). JMLR Workshop and Conference Proceedings Vol 22, p.91-99, AISTATS 2012
S. Le Corff, G. Fort and E. Moulines. New Online-EM algorithms for general Hidden Markov models. Application to the SLAM, Proceedings of the 10th International Conference on Latent Variable Analysis and Signal Separation (LVA-ICA), Springer-Verlag Berlin, Heidelberg pages 131-138, 2012.
P. Etoré, G. Fort, B. Jourdain and E. Moulines. On adaptive stratification. Annals of Operations Research 189(1):127-154, 2011. ArXiv math.PR/0809.1135
P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Performance Analysis of a Distributed Robbins-Monro algorithm for Sensor Networks. Proceedings of the 19th European Signal Processing Conference (EUSIPCO), pages 1030-1034, 2011.
Y. Atchadé, G. Fort, E. Moulines and P. Priouret. In D. Barber, A. T. Cemgil and S. Chiappia, editors. Bayesian Time Series Models, Cambridge Univ. Press, 2011. Chapter 2 : Adaptive Markov chain Monte Carlo : Theory and Methods, 33-53.
S. Le Corff, G. Fort and E. Moulines. Un algorithme EM récursif pour le SLAM. Proceedings du Groupe d’Etudes du Traitement du Signal et des Images (GRETSI), 2011.
P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Sur un algorithme de Robbins-Monro distribué. Proceedings du Groupe d’Etudes du Traitement du Signal et des Images (GRETSI), 2011.
S. Le Corff, G. Fort and E. Moulines. Online Expectation-Maximization algorithm to solve the SLAM problem, Proceedings of the 2011 IEEE Statistical Signal Processing Workshop (SSP), pages 225-228, 2011.
S. Le Corff and G. Fort. Block Online EM for Hidden Markov Models with general state space, 2011. Proceedings of International Conference Applied Stochastic Models and Data Analysis (ASMDA), 2011.
P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Convergence of a distributed parameter estimator for sensor network with local averaging of the estimates. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 3764-3767, 2011.
M. Kilbinger, D. Wraith, C. P. Robert, K. Benabed, O. Cappé, J.F.Cardoso, G. Fort, S. Prunet, and F.R.Bouchet. Bayesian model comparison in cosmology with Population Monte Carlo. MNRAS 405(4):2381-2390, 2010. ArXiv astro-ph.CO/0912.1614
Y. Atchadé and G. Fort. Limit theorems for some adaptive MCMC algorithms with subgeometric kernels. Bernoulli 16(1):116-154, 2010. ArXiv math.PR/0807.2952
S. Connor and G. Fort. State-dependent Foster-Lyapunov criteria for subgeometric convergence of Markov chains. Stoch. Processes Appl., 119:4176-4193, 2009 ArXiv math.PR/0901.2453
D.Wraith, M. Kilbinger, K. Benabed, O. Cappé, J.F. Cardoso, G. Fort, S. Prunet and C.P. Robert. Estimation of cosmological parameters using adaptive importance sampling. Phys. Rev. D. 80(2), 2009.
R. Douc, G. Fort, E. Moulines and P. Priouret. Forgetting of the initial distribution for Hidden Markov Models. Stoch. Process Appl. 119(4):1235-1256, 2009. ArXiv.math.ST/0703836.
R. Douc, G. Fort and A. Guillin. Subgeometric rates of convergence of f-ergodic strong Markov processes. Stoch. Process Appl., 119(3):897-923, 2009. ArXiv math.ST/0605791
G. Fort, S. Meyn, E. Moulines and P. Priouret. The ODE method for the stability of skip-free Markov Chains with applications to MCMC. Ann. Appl. Probab. 18(2) :664-707, 2008.
F. Forbes and G. Fort. Combining Monte Carlo and Mean-Field-Like Methods for Inference in Hidden Markov Random Fields. IEEE Trans on Image Processing, 16(3):824(837, 2007.
G. Fort, S. Meyn, E. Moulines and P. Priouret. ODE methods for Markov chain stability with applications to MCMC. Proceedings of the 1st International Conference on Performance Evaluation Methodologies and Tools, Valuetools, Art. 42, 2006.
G. Fort, S. Lambert-Lacroix, J. Peyre. Réduction de dimension dans les modèles généralisés : application à la classification de données issues de biopuces. Journal de la SFDS, 146(1-2):117-152, 2005. Matlab code and Data set. Erratum on the research report TR0471.
G. Fort and S. Lambert-Lacroix. Classification using Partial Least Squares with Penalized Logistic Regression. Bioinformatics, 21(7):1104-1111, 2005. Matlab codes and Data set.
G. Fort and G.O. Roberts. Subgeometric ergodicity of strong Markov processes. Ann. Appli. Probab. 15(2):1565-1589, 2005.
G. Fort, E. Moulines and P. Soulier. In O. Cappé, E. Moulines and T. Ryden, editors. Inference in Hidden Markov Models, Springer 2005. Chapter 14: Elements of Markov Chain Theory, 511-562.
R. Douc, G. Fort, E. Moulines and P. Soulier. Practical drift conditions for subgeometric rates of convergence. Ann. Appl. Probab. 14(3):1353-1377, 2004.
G. Fort and S. Lambert-Lacroix. Ridge-Partial Least Squares for Generalized Linear Models with binary response. COMPSTAT’04, Proceedings in Computational Statistics, pages 1019-1026, 2004. paper
G. Fort, E. Moulines, G.O. Roberts and J.S. Rosenthal. On the geometric ergodicity of hybrid samplers. J. Appl. Probab. 40(1):123-146, 2003.
G. Fort and E. Moulines. Polynomial ergodicity of Markov transition kernels. Stoch. Process Appl. 103(1),57-99, 2003.
G. Fort and E. Moulines. Convergence of the Monte Caro EM for curved exponential families. Ann. Stat. 31(4):1220-1259, 2003.
G. Fort and E. Moulines. V-subgeometric ergodicity for a Hastings-Metropolis algorithm. Stat. Probab. Lett. 49(4):401-410, 2000.
G. Fort and E. Moulines, and P. Soulier. On the convergence of iterated random maps with applications to the MCEM algorithm. Computational Statistics, August, 1998.
G. Fort, O. Cappé, E. Moulines, and P. Soulier. Optimization via simulation for maximum likelihood estimation in incomplete data models. In Proc. IEEE Workshop on Stat. Signal and Array Proc., pages 80-83, 1998.