Pierre-Alexandre Mattei

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Jes FrellsenAssociate Professor, Technical University of Denmark (DTU)Verified email at dtu.dk
Charles BouveyronProfessor of Statistics & AI, Director of Institut 3IA Côte d'Azur, Inria, Université Côte d'AzurVerified email at inria.fr
Pierre LatoucheLaboratoire LMBP, Université Clermont Auvergne, Institut Universitaire de France (IUF)Verified email at math.cnrs.fr
Hugo SchmutzPhd student, INRIA, Université côte d'AzurVerified email at inria.fr
Benoit CarryLagrange, Observatoire de la Côte d'AzurVerified email at oca.eu
Frédéric PreciosoUniversite Cote d'AzurVerified email at unice.fr
Warith HarchaouiDERAISON.aiVerified email at deraison.ai
Niels IpsenTechnical University of DenmarkVerified email at dtu.dk
Olivier HumbertPU-PH, Centre Antoine Lacassagne, université Côte d'Azur, NiceVerified email at unice.fr
Louis OhlPostdoc, Linköping UniversityVerified email at liu.se
Hugo SenetaireTechnical University of DenmarkVerified email at dtu.dk
Mickael LeclercqPhD Computer Science & BioinformaticsVerified email at mail.mcgill.ca
Julien ChiquetUniversité Paris-Saclay, INRAE, AgroParisTechVerified email at inrae.fr
Umberto PicchiniDept. Mathematical Sciences, University of Gothenburg and Chalmers University of TechnologyVerified email at chalmers.se
Federico BergaminTechnical University of DenmarkVerified email at dtu.dk
Fanny OrlhacLITO, Inserm, Institut CurieVerified email at inserm.fr
Nicholas AyacheResearch Director at INRIA/ CSO of 3IA Côte d'AzurVerified email at inria.fr
Brendan MurphyUniversity College DublinVerified email at ucd.ie
Michael FopLecturer/Assistant Professor University College DublinVerified email at ucd.ie

Pierre-Alexandre Mattei

Research scientist, Inria, Université Côte d'Azur

Verified email at inria.fr - Homepage

Statistics Machine learning Latent variable models Deep generative models


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MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets PA Mattei, J Frellsen International Conference on Machine Learning, 4413-4423, 2019	348	2019
not-MIWAE: Deep generative modelling with missing not at random data NB Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2021	77	2021
Leveraging the exact likelihood of deep latent variable models PA Mattei, J Frellsen Advances in Neural Information Processing Signals 31, 3859-3870, 2018	77	2018
Asteroid taxonomy from cluster analysis of spectrometry and albedo M Mahlke, B Carry, PA Mattei Astronomy & Astrophysics 665, A26, 2022	61	2022
How to deal with missing data in supervised deep learning? N Ipsen, PA Mattei, J Frellsen International Conference on Learning Representations, 2022	47	2022
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	39	2019
Deep adversarial Gaussian mixture auto-encoder for clustering W Harchaoui, PA Mattei, C Bouveyron OpenReview preprint, 2017	37	2017
Bayesian variable selection for globally sparse probabilistic PCA C Bouveyron, P Latouche, PA Mattei Electronic Journal of Statistics 12 (2), 3036-3070, 2018	31	2018
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	23	2022
Exact dimensionality selection for Bayesian PCA C Bouveyron, P Latouche, PA Mattei Scandinavian Journal of Statistics 47 (1), 196-211, 2020	23	2020
Don’t fear the unlabelled: safe semi-supervised learning via debiasing H Schmutz, O Humbert, PA Mattei International Conference on Learning Representations, 2023	22*	2023
Globally sparse probabilistic PCA PA Mattei, C Bouveyron, P Latouche International Conference on Artificial Intelligence and Statistics, 976-984, 2016	19	2016
A parsimonious tour of bayesian model uncertainty PA Mattei arXiv preprint arXiv:1902.05539, 2019	16	2019
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	16	2016
Refit your encoder when new data comes by PA Mattei, J Frellsen 3rd NeurIPS workshop on Bayesian Deep Learning, 2018	12	2018
Multiplying a Gaussian matrix by a Gaussian vector PA Mattei Statistics & Probability Letters 128, 67-70, 2017	10	2017
Tensor decomposition for learning Gaussian mixtures from moments R Khouja, PA Mattei, B Mourrain Journal of Symbolic Computation 113, 193-210, 2022	8	2022
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	8	2019
Are labels informative in semi-supervised learning? Estimating and leveraging the missing-data mechanism. A Sportisse, H Schmutz, O Humbert, C Bouveyron, PA Mattei International Conference on Machine Learning, 32521-32539, 2023	7	2023
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models I Balelli, A Sportisse, F Cremonesi, PA Mattei, M Lorenzi arXiv preprint arXiv:2304.08054, 2023	6	2023

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