G. Fort, A. Juditsky and J. Liang. Stochastic Mirror Descent under Dependent Observations; submitted in Oct 2026.
T.K. Tran, T.T. Nguyen, G. Fort, T. Doan, H.D. Nguyen, B.T. Nguyen, F. Forbes and C. Drovandi. Revisiting Incremental Stochastic Majorization Minimization Algorithms with Applications to Mixture of Experts; submitted in Feb 2026.
A. Dieuleveut, G. Fort, M. Hegazy and H-T. Wai. Federated Majorize-Minimization : beyond parameter aggregation (former title: Federated Majorize-Minimization for large scale learning). 1st submission in May 2022; revised in July 2025.
G. Fort, E. Moulines, P. Gach. Fast Incremental Expectation Maximization algorithm: √n iterations for an ε-stationary point ?, March 2020. HAL-02509621. (submitted to SSP 2020; conference canceled due to CoVID-19; the paper is no more submitted).
J.F. Aujol, C. Dossal, G. Fort, E. Moulines. Rates of Convergence of Perturbed FISTA-based algorithms. July 2019. HAL-02182949.
Y. Atchadé, G. Fort, E. Moulines. On stochastic Proximal-Gradient algorithms. February 2014. ArXiv:1402.2365v1 (part of this paper is unpublished).
G. Fort. Fluid limit-based tuning of some hybrid MCMC samplers. Dec 2007.
C. Andrieu and G. Fort. Explicit control of subgeometric ergodicity. Rapport de Recherche, 05:17, 2005.
G. Fort. Partial Least Squares for classification and feature selection in Microarray gene expression data. Dec. 2004.
G. Fort. Computable bounds for V-geometric ergodicity of Markov transition kernels. Rapport de Recherche, Univ. J. Fourier, RR 1047-M.