CEREMADE
Rivoirard Vincent
Full Professor
Biography
Vincent Rivoirard has been Professor at the University Paris Dauphine since 2010 after having been Associate Professor at the University of Paris Sud Orsay between 2003 and 2010. He defended his thesis in statistics in 2002 under the supervision of Dominique Picard. His research interests cover non-parametric and high dimension statistics for Bayesian and frequentist estimation. He is interested in statistical applications in neuroscience, genetics and biology. He was Director of Ceremade between November 1, 2016 and December 31, 2022
Publications
Articles
Sulem D., Rivoirard V., Rousseau J. (2024), Bayesian estimation of nonlinear Hawkes processes, Bernoulli, vol. 30, n°2, p. 1257–1286 
Nguyen T., Pham Ngoc T., Rivoirard V. (2023), Adaptive warped kernel estimation for nonparametric regression with circular responses, Electronic Journal of Statistics, vol. 17, n°2, p. 4011 - 4048
Varet S., Lacour C., Massart P., Rivoirard V. (2023), Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation, ESAIM. Probability and Statistics, vol. 27, p. 621 - 667
BONNET A., Lacour C., Picard F., Rivoirard V. (2022), Uniform Deconvolution for Poisson Point Processes, Journal of Machine Learning Research, vol. 23, n°194, p. 1−36
Maïda M., Dat Nguyen T., Pham Ngoc T., Rivoirard V., Tran V-C. (2022), Statistical deconvolution of the free Fokker-Planck equation at fixed time, Bernoulli, vol. 28, n°2, p. 771-802
Hoang V., Pham Ngoc T., Rivoirard V., Tran V. (2022), Nonparametric estimation of the fragmentation kernel based on a PDE stationary distribution approximation, Scandinavian Journal of Statistics, vol. 49, n°1, p. 4-43
Nguyen M-L., Lacour C., Rivoirard V. (2022), Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation, Journal of Machine Learning Research, vol. 23, n°254, p. 1−74
Browning R., Sulem D., Mengersen K., Rivoirard V., Rousseau J. (2021), Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19, PLoS ONE, vol. 16, n°4
Donnet S., Rivoirard V., Rousseau J. (2020), Nonparametric Bayesian estimation for multivariate Hawkes processes, Annals of Statistics, vol. 48, n°5, p. 2698-2727
Hunt X., Reynaud-Bouret P., Rivoirard V., Sansonnet L., Willett R. (2019), A Data-Dependent Weighted LASSO Under Poisson Noise, IEEE Transactions on Information Theory, vol. 65, n°3, p. 1589-1613
Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2018), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures, Bernoulli, vol. 24, n°1, p. 231-256
Lambert R., Tuleau-Malot C., Bessaih T., Rivoirard V., Bouret Y., Leresche N., Reynaud-Bouret P. (2018), Reconstructing the functional connectivity of multiple spike trains using Hawkes models, Journal of Neuroscience Methods, vol. 297, n°1 March 2018, p. 9-21
Chichignoud M., Hoang V., Pham Ngoc T., Rivoirard V. (2017), Adaptive wavelet multivariate regression with errors in variables, Electronic Journal of Statistics, vol. 11, n°1, p. 682-724
Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2017), Posterior concentration rates for counting processes with Aalen multiplicative intensities, Bayesian Analysis, vol. 12, n°1, p. 53-87
Lacour C., Massart P., Rivoirard V. (2017), Estimator selection: a new method with applications to kernel density estimation, Sankhya, vol. 79, n°2, p. 298-335
Bertin K., Lacour C., Rivoirard V. (2016), Adaptive pointwise estimation of conditional density function, Annales Henri Poincaré, vol. 52, n°2, p. 939-980
Ivanoff S., Picard F., Rivoirard V. (2016), Adaptive Lasso and group-Lasso for functional Poisson regression, Journal of Machine Learning Research, vol. 17, p. 1-46
Hansen N., Reynaud-Bouret P., Rivoirard V. (2015), Lasso and probabilistic inequalities for multivariate point processes, Bernoulli, vol. 21, n°1, p. 83-143
Arribas-Gil A., Bertin K., Rivoirard V., Meza C. (2014), LASSO-type estimators for Semiparametric Nonlinear Mixed-Effects Models Estimation, Statistics and Computing, vol. 24, n°3, p. 443-460
Grammont F., Tuleau-Malot C., Rivoirard V., Reynaud-Bouret P. (2014), Goodness-of-fit tests and nonparametric adaptive estimation for spike train analysis, The Journal of Mathematical Neuroscience, vol. 4, n°1
Pham Ngoc T., Rivoirard V. (2013), The dictionary approach for spherical deconvolution, Journal of Multivariate Analysis, vol. 115, p. 138-156
Rousseau J., Rivoirard V. (2012), Bernstein–von Mises theorem for linear functionals of the density, Annals of Statistics, vol. 40, n°3, p. 1489-1523
Rivoirard V., Reynaud-Bouret P., Hoffmann M., Doumic Jauffret M. (2012), Nonparametric estimation of the division rate of a size-structured population, SIAM Journal on Numerical Analysis, vol. 50, n°2, p. 925-950
Rivoirard V., Rousseau J. (2012), Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, n°2, p. 311-334
Bertin K., Le Pennec E., Rivoirard V. (2011), Adaptive Dantzig density estimation, Annales Henri Poincaré, vol. 47, n°1, p. 43-74
Reynaud-Bouret P., Rivoirard V., Tuleau-Malot C. (2011), Adaptive density estimation: A curse of support?, Journal of Statistical Planning and Inference, vol. 141, n°1, p. 115-139
Reynaud-Bouret P., Rivoirard V. (2010), Near optimal thresholding estimation of a Poisson intensity on the real line, Electronic Journal of Statistics, vol. 4, p. 172-238
Autin F., Le Pennec E., Loubes J., Rivoirard V. (2010), Maxisets for Model Selection, Constructive Approximation, vol. 31, n°2, p. 195-229
Bertin K., Rivoirard V. (2009), Maxiset in sup-norm for kernel estimators, Test, vol. 18, n°3, p. 475-496
Loubes J-M., Rivoirard V. (2009), Review of rates of convergence and regularity conditions for inverse problems, International Journal of Tomography & Statistics, vol. 11, n°S09
Ouvrages
Stoltz G., Rivoirard V. (2009), Statistique en action, Paris: Vuibert, 320 p.
Communications avec actes
Reynaud-Bouret P., Rivoirard V., Tuleau-Malot C. (2013), Inference of functional connectivity in Neurosciences via Hawkes processes, in , Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE, Austin, IEEE - Institute of Electrical and Electronics Engineers
Communications sans actes
Donnet S., Rousseau J., Rivoirard V. (2014), Non parametric Bayesian estimation for Hawkes processes, International Society for Bayesian Analysis World Meeting, ISBA 2014, Cancun, Mexique
Donnet S., Rousseau J., Rivoirard V., Scricciolo C. (2014), On Convergence Rates of Empirical Bayes Procedures, SIS 2014, Cagliari, Italie
Malot C., Reynaud-Bouret P., Rivoirard V., Grammont F. (2013), Tests d'adéquation pour les processus de Poisson et les processus de Hawkes, 45ème Journées de Statistique, Toulouse, France
Prépublications / Cahiers de recherche
Nguyen T-D., Pham Ngoc T., Rivoirard V. (2022), Adaptive warped kernel estimation for nonparametric regression with circular responses, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 25 p.
Belhakem M., Picard F., Rivoirard V., Roche A. (2021), Minimax estimation of Functional Principal Components from noisy discretized functional data, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 35 p.
Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2014), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures. Supplementary material, Paris, Université Paris-Dauphine, 4 p.