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Pierre-Alexandre Mattei
Pierre-Alexandre Mattei
Research scientist, Inria
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets
PA Mattei, J Frellsen
International Conference on Machine Learning, 4413-4423, 2019
1492019
Leveraging the exact likelihood of deep latent variable models
PA Mattei, J Frellsen
Advances in Neural Information Processing Signals 31, 3859-3870, 2018
412018
Bayesian variable selection for globally sparse probabilistic PCA
C Bouveyron, P Latouche, PA Mattei
Electronic Journal of Statistics 12 (2), 3036-3070, 2018
252018
not-MIWAE: Deep generative modelling with missing not at random data
NB Ipsen, PA Mattei, J Frellsen
International Conference on Learning Representations, 2021
222021
Deep adversarial Gaussian mixture auto-encoder for clustering
W Harchaoui, PA Mattei, C Bouveyron
222017
Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation
S Wiqvist, PA Mattei, U Picchini, J Frellsen
International Conference on Machine Learning, 6798-6807, 2019
182019
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
162016
Exact dimensionality selection for Bayesian PCA
C Bouveyron, P Latouche, PA Mattei
Scandinavian Journal of Statistics 47 (1), 196-211, 2020
152020
How to deal with missing data in supervised deep learning?
N Ipsen, PA Mattei, J Frellsen
Artemiss-ICML Workshop on the Art of Learning with Missing Values, 2020
122020
Globally sparse probabilistic PCA
PA Mattei, C Bouveyron, P Latouche
Artificial Intelligence and Statistics, 976-984, 2016
112016
Refit your encoder when new data comes by
PA Mattei, J Frellsen
3rd NeurIPS workshop on Bayesian Deep Learning, 2018
82018
Multiplying a Gaussian matrix by a Gaussian vector
PA Mattei
Statistics & Probability Letters 128, 67-70, 2017
82017
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
62019
A parsimonious tour of bayesian model uncertainty
PA Mattei
arXiv preprint arXiv:1902.05539, 2019
52019
Tensor decomposition for learning Gaussian mixtures from moments
R Khouja, PA Mattei, B Mourrain
Journal of Symbolic Computation 113, 193-210, 2022
32022
Unobserved classes and extra variables in high-dimensional discriminant analysis
M Fop, PA Mattei, C Bouveyron, TB Murphy
Advances in Data Analysis and Classification 16 (1), 55-92, 2022
32022
Model-agnostic out-of-distribution detection using combined statistical tests
F Bergamin, PA Mattei, JD Havtorn, H Senetaire, H Schmutz, L Maaløe, ...
International Conference on Artificial Intelligence and Statistics, 10753-10776, 2022
22022
Wasserstein adversarial mixture clustering
W Harchaoui, A Almansa, PA Mattei, C Bouveyron
2*2018
Sélection de modèles parcimonieux pour l’apprentissage statistique en grande dimension
PA Mattei
Sorbonne Paris Cité, 2017
12017
Model selection for sparse high-dimensional learning
PA Mattei
Université Paris 5, 2017
12017
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