Pierre-Alexandre Mattei
Pierre-Alexandre Mattei
Postdoctoral fellow, IT University of Copenhagen
Verified email at itu.dk - Homepage
TitleCited byYear
Bayesian variable selection for globally sparse probabilistic PCA
C Bouveyron, P Latouche, PA Mattei
Electronic Journal of Statistics 12 (2), 3036-3070, 2018
Combining a relaxed EM algorithm with Occam’s razor for Bayesian variable selection in high-dimensional regression
P Latouche, PA Mattei, C Bouveyron, J Chiquet
Journal of Multivariate Analysis 146, 177-190, 2016
Leveraging the exact likelihood of deep latent variable models
PA Mattei, J Frellsen
Advances in Neural Information Processing Systems, 3855-3866, 2018
Globally sparse probabilistic PCA
PA Mattei, C Bouveyron, P Latouche
Artificial Intelligence and Statistics, 976-984, 2016
Exact dimensionality selection for Bayesian PCA
C Bouveyron, P Latouche, PA Mattei
arXiv preprint arXiv:1703.02834, 2017
Multiplying a Gaussian matrix by a Gaussian vector
PA Mattei
Statistics & Probability Letters 128, 67-70, 2017
Deep adversarial Gaussian mixture auto-encoder for clustering
W Harchaoui, PA Mattei, C Bouveyron
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
PA Mattei, J Frellsen
International Conference on Machine Learning, 4413-4423, 2019
Class‐specific variable selection in high‐dimensional discriminant analysis through Bayesian Sparsity
F Orlhac, PA Mattei, C Bouveyron, N Ayache
Journal of Chemometrics 33 (2), e3097, 2019
Refit your Encoder when New Data Comes by
PA Mattei, J Frellsen
3rd NeurIPS workshop on Bayesian Deep Learning, 2018
Model selection for sparse high-dimensional learning
PA Mattei
A Parsimonious Tour of Bayesian Model Uncertainty
PA Mattei
arXiv preprint arXiv:1902.05539, 2019
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
S Wiqvist, PA Mattei, U Picchini, J Frellsen
arXiv preprint arXiv:1901.10230, 2019
Wasserstein Adversarial Mixture Clustering
W Harchaoui, A Almansa, PA Mattei, C Bouveyron
Deep latent variable models
PA Mattei
Séminaire de statistique du CNAM, 2018
Leveraging the Exact Likelihood of Deep Latent Variable Models–Appendices
PA Mattei, J Frellsen
Supplementary Material - NeurIPS 2018, 2018
Sélection de modèles parcimonieux pour l’apprentissage statistique en grande dimension
PA Mattei
Sorbonne Paris Cité, 2017
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets supplementary material
PA Mattei, J Frellsen
Unobserved classes and extra variables in high-dimensional discriminant analysis
M Fop, PA Mattei, TB Murphy, C Bouveyron
CASI 2018, 70, 0
Une relaxation continue du rasoir d’Ockham pour la régression en grande dimension
PA Mattei, P Latouche, C Bouveyron, J Chiquet
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