Sub-sections



Journal Paper   
  1. 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.
  2. G. Morral, P. Bianchi and G. Fort. Success and Failure of Adaptation-Diffusion Algorithms for Consensus in Multi-Agent Networks. arXiv CS.MA 1410.6956, Revised in Oct15 and Sept16. Accepted for publication in IEEE Trans. Signal Processing, January 2017.
  3. Y. Atchadé, G. Fort and E. Moulines. On perturbed proximal gradient algorithms, Submitted, February 2014 under the title "On stochastic proximal gradient algorithms". arXiv:1402:2365 math.ST.  Revised in Jan16 and Nov16. Accepted for publication in JMLR, November 2016.
  4. 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.
  5. H. Braham, S. Ben Jemaa, G. Fort, E. Moulines and B. Sayrac. Fixed Rank Kriging for Cellular Coverage Analysis. arXiv:1505:07062, Accepted for publication in IEEE Trans. Vehicular Technology, July 2016.
  6. A. Schreck, G. Fort, E. Moulines and M. Vihola.Convergence of Markovian Stochastic Approximation with discontinuous dynamics . arXiv math.ST 1403.6803, submitted in March 2014. SIAM J. Control Optim.,54(2):866-893, 2016.
  7. 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.
  8. 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.
  9. A. Durmus, G. Fort and E. Moulines. Subgeometric rates of convergence rates in Wasserstein distance for Markov chains. arXiv:1402.4577 math.PR. Accepted for publication in Ann. Inst. Henri Poincaré, 52(4):1799-1822, 2016.
  10. G. Fort. Central Limit Theorems for Stochastic Approximation with Controlled Markov Chain Dynamics EsaimPS, 19:60-80, 2015.  arXiv math.PR 1309.3116
  11. C. 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
  12. 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]
  13. 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.
  14. 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]
  15. P. Bianchi, G. Fort and W. Hachem.  Performance of a Distributed Stochastic Approximation Algorithm IEEE Trans. on Information Theory, 59(11):7405-7418, 2013.
  16. 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.
  17. 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.
  18. 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.
  19. 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.
  20. 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.
  21. 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],
  22. Y. Atchadé and G. Fort. Limit theorems for some adaptive MCMC algorithms with subgeometric kernels, part II. Bernoulli 18(3):975-1001, 2012.
  23. 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
  24. 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
  25. 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  
  26. S. Connor and G. Fort. State-dependent Foster-Lyapunov criteria for subgeometric convergence of Markov chains. Stochastic Processes Appl. 119:4176-4193, 2009  ArXiv  math.PR/0901.2453
  27. 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 samplingPhys.Rev. D. 80(2), 2009.  ArXiv stat.CO/0903.0837
  28. 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 
  29. 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
  30. 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.
  31. F. Forbes and G. Fort. A convergence theorem for Variational EM-like algorithms : application to image segmentation. IEEE Transactions on Image Processing, 16(3):824-837,2007  MATLAB Codes   
  32. G. Fort, S. Lambert-Lacroix, J. Peyre. Réduction de dimension dans les modèles généralisés : Application à  la classification de données issues des biopuces. Journal de la SFDS, 146(1-2):117-152,2005.  Matlab codes and Data setErratum on the research report TR0471
  33. 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.
  34. G. Fort and G. O. Roberts. Subgeometric ergodicity of strong Markov processes.  Ann. Appl. Probab. 15(2):1565-1589, 2005.
  35. R. Douc, G. Fort, E. Moulines and P. Soulier. Practical drift conditions for subgeometric rates of convergenceAnn. Appl. Probab. 14(3) :1353-1377, 2004.
  36. 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.
  37. G. Fort and E. Moulines. Polynomial ergodicity of Markov transition kernels. Stochastic Processes Appl. 103(1):57-99, 2003
  38. G. Fort and E. Moulines. Convergence of the Monte-Carlo EM for curved exponential families.   Ann. Stat. 31(4):1220-1259, 2003. 
  39. G. Fort and E. Moulines. V-subgeometric ergodicity for a Hastings-Metropolis algorithm. Stat. Probab. Lett. 49(4):401-410,2000.
Chapters in books  
  1. 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.
  2. 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.
Conference Proceedings
  1. G. Fort, L. Risser, E. Moulines, E. Ollier and A. Leclerc-Samson. Algorithmes Gradient-Proximaux stochastiques. GRETSI, September 2017.
  2. G. Morral, P. Bianchi and G. Fort. Success and Failure of Adaptation-Diffusion Algorithms for Consensus in Multiagent Networks. Accepted for publication in the proceedings of the 53rd IEEE Conference on Decision and Control (CDC 2014), December 2014.
  3. 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.
  4. 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.
  5. G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Approximation stochastique distribuée : le coût de la non bistochasticité. GRETSI, September 2013.
  6. G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Distributed Stochastic Approximation: The Price of Non-double Stochasticity. ASILOMAR November 2012.
  7. 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
  8. 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.
  9. P. Bianchi, G. Fort, W. Hachem and J. Jakubowicz. Performance Analysis of a Distributed On-Line Estimator for Sensor Networks. Proceedings of the 19th European Signal Processing Conference (EUSIPCO),  pages 1030-1034, 2011.
  10. S. Le Corff, G. Fort and E. Moulines. Un algorithme EM récursif pour le SLAMProceedings du Groupe d'Etudes du Traitement du Signal et des Images (GRETSI), 2011.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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. 
  17. 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.
  18. 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.
Technical Report
  1. G. Fort, E. Ollier and A. Leclerc-Samson. Stochastic Proximal Gradient Algorithms for Penalized Mixed Models. Submitted, April 2017. arXiv:1704.08891
  2. G. Fort, B. Jourdain, T. Lelièvre and G. Stoltz. Convergence and Efficiency of Adaptive Importance Sampling techniques with partial biasing.  Submitted, November 2016. Revised in July 2017.
  3. G. Fort. Fluid limit-based tuning of some hybrid MCMC samplers. Dec 2007.
  4. C. Andrieu and G. Fort. Explicit control of subgeometric ergodicity. Rapport de Recherche, 05:17, 2005.
  5. G. Fort. Partial Least Squares for classification and feature selection in Microarray gene expression data. Dec. 2004.
  6. G. Fort. Computable bounds for V-geometric ergodicity of Markov transition kernels. Rapport de Recherche, Univ. J. Fourier, RR 1047-M.
Works in progress
  1. with B. Jourdain, T. Lelièvre and G. Stoltz - about convergence of Monte Carlo algorithms for molecular dynamics.
  2. with A. Leclerc-Samson and E. Ollier - about stochastic EM for non-smooth penalized log-likelihood.
  3. with E. Moulines - about non convex stochastic optimization.
  4. with S. Crepey and E. Gobet  - about Monte Carlo methods for rare event sampling.
  5. with J.F. Aujol, C. Dossal, and E. Moulines - about stochastic proximal gradient algorithms.
PhD Thesis and HDR
  1. G. Fort. Habilitation à  Diriger les Recherches "Méthodes de Monte Carlo et Chaînes de Markov pour la simulation". Univ. Paris Dauphine, Feb. 2010. (website)
  2. G. Fort. PhD thesis. "Contrôle explicite d'ergodicité de chaînes de Markov : application à  l'analyse de convergence de l'algorithme  Monte Carlo EM". Univ. Paris VI, June 2001. Inist Number : T139824