Globally Sparse Probabilistic PCA. - Pierre-Alexandre Mattei; Charles Bouveyron; Pierre Latouche (AISTATS, 2016)

Combining a relaxed EM algorithm with Occam's razor for Bayesian variable selection in high-dimensional regression. - Pierre Latouche; Pierre-Alexandre Mattei; Charles Bouveyron; Julien Chiquet (J. Multivar. Anal., 2016)

Leveraging the Exact Likelihood of Deep Latent Variable Models. - Pierre-Alexandre Mattei; Jes Frellsen (NeurIPS, 2018)

missIWAE: Deep Generative Modelling and Imputation of Incomplete Data. - Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2018)

MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets. - Pierre-Alexandre Mattei; Jes Frellsen (ICML, 2019)

Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation. - Samuel Wiqvist; Pierre-Alexandre Mattei; Umberto Picchini; Jes Frellsen (ICML, 2019)

not-MIWAE: Deep Generative Modelling with Missing not at Random Data. - Niels Bruun Ipsen; Pierre-Alexandre Mattei; Jes Frellsen (ICLR, 2021)

Model-agnostic out-of-distribution detection using combined statistical tests. - Federico Bergamin; Pierre-Alexandre Mattei; Jakob Drachmann Havtorn; Hugo Sénétaire; Hugo Schmutz; Lars Maaløe; Søren Hauberg; Jes Frellsen (AISTATS, 2022)

How to deal with missing data in supervised deep learning? - Niels Bruun Ipsen; Pierre-Alexandre Mattei; Jes Frellsen (ICLR, 2022)

Generalised Mutual Information for Discriminative Clustering. - Louis Ohl; Pierre-Alexandre Mattei; Charles Bouveyron; Warith Harchaoui; Mickaël Leclercq; Arnaud Droit; Frédéric Precioso (NeurIPS, 2022)

Unobserved classes and extra variables in high-dimensional discriminant analysis. - Michael Fop; Pierre-Alexandre Mattei; Charles Bouveyron; Thomas Brendan Murphy (Adv. Data Anal. Classif., 2022)

Uphill Roads to Variational Tightness: Monotonicity and Monte Carlo Objectives. - Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2022)

Don't fear the unlabelled: safe deep semi-supervised learning via simple debiasing. - Hugo Schmutz; Olivier Humbert; Pierre-Alexandre Mattei (CoRR, 2022)

A Multi-stage deep architecture for summary generation of soccer videos. - Melissa Sanabria; Frédéric Precioso; Pierre-Alexandre Mattei; Thomas Menguy (CoRR, 2022)

Tensor decomposition for learning Gaussian mixtures from moments. - Rima Khouja; Pierre-Alexandre Mattei; Bernard Mourrain (J. Symb. Comput., 2022)

Don't fear the unlabelled: safe semi-supervised learning via debiasing. - Hugo Schmutz; Olivier Humbert; Pierre-Alexandre Mattei (ICLR, 2023)

Explainability as statistical inference. - Hugo Henri Joseph Senetaire; Damien Garreau; Jes Frellsen; Pierre-Alexandre Mattei (ICML, 2023)

Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. - Aude Sportisse; Hugo Schmutz; Olivier Humbert; Charles Bouveyron; Pierre-Alexandre Mattei (ICML, 2023)

Sparse GEMINI for Joint Discriminative Clustering and Feature Selection. - Louis Ohl; Pierre-Alexandre Mattei; Charles Bouveyron; Mickaël Leclercq; Arnaud Droit; Frédéric Precioso (CoRR, 2023)

Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models. - Irene Balelli; Aude Sportisse; Francesco Cremonesi; Pierre-Alexandre Mattei; Marco Lorenzi (CoRR, 2023)

Generalised Mutual Information: a Framework for Discriminative Clustering. - Louis Ohl; Pierre-Alexandre Mattei; Charles Bouveyron; Warith Harchaoui; Mickaël Leclercq; Arnaud Droit; Frédéric Precioso (CoRR, 2023)

Are ensembles getting better all the time? - Pierre-Alexandre Mattei; Damien Garreau (CoRR, 2023)

Kernel KMeans clustering splits for end-to-end unsupervised decision trees. - Louis Ohl; Pierre-Alexandre Mattei; Mickaël Leclercq; Arnaud Droit; Frédéric Precioso (CoRR, 2024)

Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection. - Louis Ohl; Pierre-Alexandre Mattei; Charles Bouveyron; Mickaël Leclercq; Arnaud Droit; Frédéric Precioso (Stat. Comput., 2024)

Learning Energy-Based Models by Self-normalising the Likelihood. - Hugo Senetaire; Paul Jeha; Pierre-Alexandre Mattei; Jes Frellsen (CoRR, 2025)

A Tutorial on Discriminative Clustering and Mutual Information. - Louis Ohl; Pierre-Alexandre Mattei; Frédéric Precioso (CoRR, 2025)

Parsimonious Gaussian mixture models with piecewise-constant eigenvalue profiles. - Tom Szwagier; Pierre-Alexandre Mattei; Charles Bouveyron; Xavier Pennec (CoRR, 2025)