Sébastien Gadat

Manon Costa, Sébastien Gadat, and Lorick Huang, “CV@R penalized portfolio optimization with biased stochastic mirror descent”, Finance and Stochastics, 2024, forthcoming.

Sébastien Gadat, and Fabien Panloup, “Optimal non-asymptotic analysis of the Ruppert-Polyak averaging stochastic algorithm”, Stochastic Processes and their Applications, vol. 156, February 2023, pp. 312–348.

Sébastien Gadat, and Ioana Gavra, “Asymptotic study of stochastic adaptive algorithm in non-convex landscape”, Journal of Machine Learning Research, n. 228, August 2022, pp. 1–54.

Sébastien Gadat, Lola Corre, Antoine Doury, Aurélien Ribes, and Samuel Somot, “Regional Climate Model Emulator Based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach”, Climate Dynamics, July 2022.

Bernard Bercu, Jérémie Bigot, Sébastien Gadat, and Emilia Siviero, “A Stochastic Gauss-Newton Algorithm for Regularized Semi-discrete Optimal Transport”, Information and Inference: A Journal of the IMA, May 2022.

Bernard Bercu, Sébastien Gadat, and Manon Costa, “Stochastic approximation algorithms for superquantiles estimation”, Electronic Journal of Probability, vol. 26, n. 84, June 2021, pp. 1–29.

Y. De Castro, Sébastien Gadat, Clément Marteau, and Cathy Maugis, “SuperMix: Sparse Regularization for Mixture”, Annals of Statistics, vol. 49, n. 3, 2021, pp. 1779–1809.

Fanny Lafouresse, Romain Jugele, Sabina Müller, Marine Doineau, Valérie Duplan-Eche, Eric Espinosa, Marie-Pierre Puissegur, Sébastien Gadat, and Salvatore Valitutti, “Stochastic asymmetric repartition of lytic machinery in dividing CD8+ T cells generates heterogeneous killing behavior”, eLife, 2021.

Sébastien Gadat, and Manon Costa, “Non-Asymptotic Study of a Recursive Superquantile Estimation Algorithm”, Electronic Journal of Statistics, vol. 15, n. 2, January 2021, pp. 4718–4769.

Sébastien Gadat, Clément Marteau, and Cathy Maugis, “Parameter recovery in two-component contamination mixtures: the L2 strategy”, Annales de l'Institut Henri Poincaré, vol. 56, n. 2, August 2020, pp. 1391–1418.

Sébastien Gadat, Sebastien Gerchinovitz, and Clément Marteau, “Optimal functional supervised classification with separation condition”, Bernoulli, vol. 26, n. 3, 2020, p. 1797–1831.

Manon Costa, Sébastien Gadat, Pauline Gonnord, and Laurent Risser, “Cytometry inference through adaptive atomic deconvolution”, Journal of Nonparametric Statistics, vol. 31, n. 2, April 2019, pp. 506–547.

Pauline Gonnord, Megan Costa, M. Peres, Sébastien Gadat, and S. Valitutti, “Patient clustering reveals CD8+ T cell central/effector memory dichotomy as an early marker of disease progression in chronic lymphocytic leukemia”, OncoImmunology, vol. 8, n. 4, 2019.

Sébastien Gadat, Ioana Gavra, and Laurent Risser, “How to calculate the barycenter of a weighted graph”, Mathematics of Operations Research, vol. 43, n. 4, November 2018, pp. 1051–1404.

Fabien Panloup, Sofiane Saadane, and Sébastien Gadat, “Regret bound for Narendra-Shapiro bandit algorithms”, Stochastics, May 2018, pp. 886–926.

Sébastien Gadat, Fabien Panloup, and Sofiane Saadane, “Stochastic Heavy Ball”, Electronic Journal of Statistics, vol. 12, n. 1, 2018, pp. 461–529.

Loic Ysebaert, Sarah Cadot, Salim Kanoun, Soleakhena Ken, Christian Recher, François Malgouyres, Sébastien Gadat, Loïc Dupré, and Anne Quillet Mary, “Computational Integration to Model Tumor Dynamics in CLL Patients Treated with the Btk Inhibitor Ibrutinib (CompuTreatCLL): First Results of an Integrative Systems Biology Approach”, Blood, vol. 130, n. 1, 2017.

Sébastien Gadat, Thierry Klein, and Clément Marteau, “Classification with the nearest neighbor rule in general finite dimensional spaces”, Annals of Statistics, vol. 44, n. 3, 2016, pp. 982–1009.

Sébastien Gadat, Laurent Miclo, and Fabien Panloup, “A stochastic model for speculative bubbles”, Alea - Latin American Journal of Probability and Mathematical Statistics, vol. 12, n. 1, November 2015.

Nicolas Chopin, Sébastien Gadat, Benjamin Guedj, Arnaud Guyader, and Elodie Vernet, “On some recent advances in high dimensional Bayesian Statistics”, Esaim Proceedings and Surveys, vol. 51, October 2015, pp. 293–319.