MATLAB codes
Paper 1 « Classification using Partial Leas Squares with Penalized logistic Regression », with S. Lambert-Lacroix
Paper 2 « Partial Least Squares for classification and feature selection in Microarray gene expression data »
Codes developed by G. Fort
Paper « A convergence theorem for Variation-EM like algorithms: Applications to Image Segmentation », with F. Forbes
Codes developed by G. Fort
Paper « On adaptive stratification », with B. Jourdain, P. Etoré and E. Moulines
Codes developed by G. Fort
Paper « Estimation of cosmological parameters using adaptive importance sampling », with D. Wraith, M. Kilbinger, K. Benabed, O. Cappé, J.F. Cardoso, S. Prunet and C.P. Robert
These codes were developed by M. Kilbinger and K. Benabed (G. Fort participated to preliminary versions of the codes,developed in Matlab).
Paper 1 « Convergence of a particle-based approximation of the Block-Online Expectation Maximization algorithm », with S. Le Corff
Paper 2 « Online Expectation Maximization based algorithms for inference in Hidden Markov Models », with S.Le Corff
Paper 3 « New Online-EM algorithms for general Hidden Markov Models. Application to the SLAM », with S. Le Corff and E. Moulines
Codes developed by S. Le Corff — during his PhD under my co-supervision.
Paper « Adaptive Equi-Energy sampler: convergence and illustration », with A. Schreck and E. Moulines
Codes developed by A. Schreck — during her PhD under my co-supervision
Paper « Adaptive Metropolis Online-Relabeling », with R. Bardenet, O. Cappé and B. Kegl
Codes developed by R. Bardenet
Paper « Shrinkage-Threshoding Metropolis-Adjusted Langevin Algorithm », with A. Schreck, S. Le Corff and E. Moulines.
Codes developed by A. Schreck — during her PhD under my co-supervision
Paper « Fast Incremental Expectation Maximization for non-convex finite-sum optimization: non asymptotic convergence bounds » with E. Moulines and P. Gach
Codes developed by G. Fort
Codes (TBA)
Paper 1 « The Perturbed Prox-Preconditioned SPIDER algorithm for EM-based large scale learning », with E. Moulines.
Paper 2 « The Perturbed Prox-Preconditioned SPIDER algorithm: non-asymptotic convergence bounds », with E. Moulines.
Paper 3 — TBA
Codes developed by G. Fort
Paper 1- « Temporal Evolution of the Covid19 pandemic reproduction number: Estimations from Proximal optimization to Monte Carlo sampling », by P. Abry, G. Fort, B. Pascal and N. Pustelnik. Accepted for publication in EMBC 2022 proceedings.
Paper 2- « Credibility Interval Design for Covid19 Reproduction Number from nonsmooth Langevin-type Monte Carlo sampling », by H. Artigas, B. Pascal, G. Fort, P. Abry and N. Pustelnik. Accepted for publication in EUSIPCO 2022 proceedings.
Paper 3- « Estimation et Intervalles de crédibilité pour le taux de reproduction de la Covid19 par échantillonnage Monte Carlo Langevin Proximal », by P. Abry, G. Fort, B. Pascal and N. Pustelnik. Accepted for publication in GRETSI 2022 proceedings.
Paper 4- « Credibility intervals for the reproduction number of the Covid-19 pandemic using Proximal Lanvevin samplers », by P. Abry, G. Fort, B. Pascal and N. Pustelnik. Accepted for publication in EUSIPCO 2023.
Paper 5- « Covid19 Reproduction Number: Credibility Intervals by Blockwise Proximal Monte Carlo samplers », by G. Fort, B. Pascal, P. Abry and N. Pustelnik. Accepted for publication in IEEE Trans Signal Processing
Codes developed by G. Fort (and some of them, with H. Artigas).
Paper 6- « Pandemic Intensity Estimation From Stochastic Approximation-Based Algorithms », by P. Abry, J. Chevallier, G. Fort and B. Pascal. Accepted for publication in CAMSAP 2023 proceedings; see also HAL version.
Paper 7- « Hierarchical Bayesian Estimation of COVID-19 Reproduction Number », by P. Abry, J. Chevallier, G. Fort and B. Pascal. Accepted for publication in ICASSP 2025 proceedings.