Full list

Full list (accepted and/or published)

• 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, 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.
  

• 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.