I am research scientist at Inria, in Sophia Antipolis, near Nice. I am part of the Maasai (Models and Algorithms for Artificial Intelligence) team. I am also affiliated with the J.A. Dieudonné lab, which is the mathematics research department of Université Côte d'Azur. I also hold a chair of the 3IA Côte d'Azur.

I received in 2017 my Ph.D. in applied mathematics from Université Paris Descartes (now called Université de Paris). I worked at the MAP5 lab, where I had the chance to be advised by Charles Bouveyron and Pierre Latouche. After my Ph.D., I did a postdoc at the IT University of Copenhagen, where I mainly worked with Jes Frellsen. My field of research is statistical machine learning, with a particular emphasis on hidden variables and model uncertainty. During my Ph.D., I mainly developed new Bayesian model selection methods for high-dimensional data. I also currently work on deep generative models and their applications.

I am one of the co-organisers of the Workshop on the Art of Learning with Missing Values (Artemiss). You can watch videos of the first edition (that was part of the ICML 2020 conference) here. I am also co-organising of the Workshop on Generative Models and Uncertainty Quantification (GenU), a small-scale workshop generally held in October in Copenhagen.

Current PhD Students


Ecole Normale Supérieure de Paris Saclay
Université Côte d'Azur
Deep latent variable models and missing data imputation
Course at the ProbAI summer school, in Trondheim, Norway, 2019

Preprints & Working papers

Asteroid Taxonomy from Cluster Analysis of Spectrometry and Albedo
(with Max Mahlke and Benoit Carry)
Preprint arXiv:2203.11229, 2022
Don't fear the unlabelled: safe deep semi-supervised learning via simple debiasing
(with Hugo Schmutz and Olivier Humbert)
Preprint arXiv:2203.07512, 2022
Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives
(with Jes Frellsen)
Preprint arXiv:2201.10989, 2022
A Parsimonious Tour of Bayesian Model Uncertainty
Preprint arXiv:1902.05539, 2020
Wasserstein Adversarial Mixture Clustering
(with Warith Harchaoui, Charles Bouveyron, and Andrés Almansa)
Preprint HAL-01827775, Université Paris Descartes, 2018



Model-agnostic out-of-distribution detection using combined statistical tests
(with Federico Bergamin, Jakob Havtorn, Hugo Senetaire, Hugo Schmutz, Lars Maaløe, Søren Hauberg, and Jes Frellsen)
Proceedings of the 25th International Conference on Artificial Intelligence and Statistics, 2022
How to deal with missing data in supervised deep learning?
(with Niels Bruun Ipsen and Jes Frellsen)
International Conference on Learning Representations, 2022
Tensor decomposition for learning Gaussian mixtures from moments
(with Rima Khouja and Bernard Mourrain)
Journal of Symbolic Computation, vol. 113, pp. 193-210, 2022
Unobserved classes and extra variables in high-dimensional discriminant analysis
(with Michael Fop, Charles Bouveyron, and Brendan Murphy)
Advances in Data Analysis and Classification, vol. 16, pp. 55-92, 2022


not-MIWAE: Deep Generative Modelling with Missing not at Random Data
(with Niels Bruun Ipsen and Jes Frellsen)
International Conference on Learning Representations, 2021


Exact Dimensionality Selection for Bayesian PCA
(with Charles Bouveyron and Pierre Latouche)
Scandinavian Journal of Statistics, vol. 47 (1), pp. 196-211, 2020


MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
(with Jes Frellsen)
Proceedings of the 36th International Conference on Machine Learning, pp. 4413-4423, 2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
(with Samuel Wiqvist, Umberto Picchini, and Jes Frellsen)
Proceedings of the 36th International Conference on Machine Learning, pp. 6798-6807, 2019
Class-specific Variable Selection in High-Dimensional Discriminant Analysis through Bayesian Sparsity
(with Fanny Orlhac, Charles Bouveyron, and Nicholas Ayache)
Journal of Chemometrics, vol. 33 (2), e3097, 2019


Leveraging the Exact Likelihood of Deep Latent Variable Models
(with Jes Frellsen)
Advances in Neural Information Processing Signals 31, pp. 3859-3870, 2018
Refit your Encoder when New Data Comes by
(with Jes Frellsen)
3rd NeurIPS workshop on Bayesian Deep Learning, 2018
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
(with Charles Bouveyron and Pierre Latouche)
Electronic Journal of Statistics, vol. 12 (2), pp. 3036-3070, 2018


Model Selection for Sparse High-Dimensional Learning
Ph.D. Thesis, Université Paris Descartes, 2017
Multiplying a Gaussian Matrix by a Gaussian Vector
Statistics & Probability Letters, vol. 128, pp. 67–70, 2017
Discussion on the Paper "A Bayesian Information Criterion for Singular Models" by Drton and Plummer
Journal of the Royal Statistical Society: Series B, vol. 79, pp. 370–371, 2017


Combining a Relaxed EM Algorithm with Occam's Razor for Bayesian Variable Selection in High-Dimensional Regression
(with Pierre Latouche, Charles Bouveyron, and Julien Chiquet)
Journal of Multivariate Analysis, vol. 146, pp. 177–190, 2016