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Conference Proceedings

  1. P. Abry,  J. Chevallier, G. Fort and B. Pascal. Pandemic Intensity Estimation From Stochastic Approximation-Based Algorithms. Ju HAL-04174245v1 Accepted for publication in CAMSAP 2023.
  2. 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
  3. 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.  March 2022. Submitted. HAL-03611891. Accepted for publication in the GRETSI 2022 proceedings.
  4. 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. Accepted for publication in the  EUSIPCO 2022 proceedings.  [video and poster, by B. Pascal]
  5. 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. Accepted for publication in EMBC 2022 proceedings.
  6. H. Duy Nguyen, F. Forbes, G. Fort and O. Cappé. An online Minorization-Maximization algorithm.  HAL-03542180. Accepted for publication in IFCS 2022 proceedings.
  7. A. Dieuleveut, G. Fort, E. Moulines, G. Robin. Federated Expectation Maximization with  heterogeneity mitigation and variance reduction, May 2021; revised in August 2021. Accepted to NeurIPS 2021. 
  8. 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)
  9. 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).
  10. 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) pp:3135-3139. With supplementary material. Matlab codes on Github.
  11. 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]
  12. S. Crepey, G. Fort, E. Gobet and U. Stazhynski. Quantification d’Incertitude pour l’Approximation Stochastique. GRETSI, August 2019.
  13. G. Fort, L. Risser, Y. Atchadé and E. Moulines. Stochastic FISTA algorithms: so fast ? . Workshop in Statistical Signal Processing (SSP), June 2018.
  14. G. Fort, L. Risser, E. Moulines, E. Ollier and A. Leclerc-Samson. Algorithmes Gradient-Proximaux stochastiques. GRETSI, September 2017.
  15. 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.
  16. 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.
  17. 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.
  18. G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Approximation stochastique distribuée : le coût de la non bistochasticité. GRETSI, September 2013.
  19. G. Morral, P. Bianchi, G. Fort and J. Jakubowicz. Distributed Stochastic Approximation: The Price of Non-double Stochasticity. ASILOMAR November 2012.
  20. 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
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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. 
  30. G. Fort and E. Moulines, and P. Soulier. On the convergence of iterated random maps with applications to the MCEM algorithm.  In R. Payne (ed.) et al., Proceedings of the 13th symposium on computational statistics. Physica-Verlag. 317-322 (COMPSTAT 1998).
  31. 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. (SSAP), pages 80-83, 1998.
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