
Pierre Humbert
Département de Mathématiques
Bâtiment 307
Faculté des Sciences d'Orsay
Université ParisSaclay
91405 Orsay Cedex
France
Courrier électronique : prenom.nom at universiteparissaclay.fr
Bureau : 2F3
Téléphone : (+33) 1 69 15 60 28



Since September 2021, I am a postdoc in the probability and statistics team of the
Laboratoire de Mathématiques d'Orsay (LMO) with
Sylvain Arlot. I am also a member of the
INRIA Celeste team.
Before that, I completed a PhD at ENS ParisSaclay entitled ``Multivariate analysis with tensors and graphs  application to neuroscience'', under the supervision of
Nicolas Vayatis,
Laurent Oudre, and
Julien Audiffren.
Main research interests
 Statistical learning, nonparametric statistics
 Crossvalidation, resampling, bootstrap
 Robust statistics
 Signal processing
 Graph and tensor learning
 Applications to neuroscience
Publications
 (2022) P. Humbert*, B. Le Bars*, L. Minvielle*.
Robust kernel density estimation with medianofmeans principle
In Proceedings of the 39th International Conference on Machine Learning (ICML), 2022.
[soon]
 (2021) P. Humbert*, B. Le Bars*, L. Oudre, A. Kalogeratos, N. Vayatis.
Learning Laplacian matrix from graph signals with sparse spectral representation
Journal of Machine Learning Research (JMLR), 22(195):147, 2021.
[journal]
[pdf]
[code]
 (2021) P. Humbert, L. Oudre, N. Vayatis, J. Audiffren.
Tensor convolutional dictionary learning with CP lowrank activations
IEEE Transactions on Signal Processing (TSP), 2021.
[journal]
[pdf]
[code]
 (2021) P. Humbert, L. Oudre, C. Dubost.
Learning spatial filters from EEG signals with graph signal processing methods
In Proceedings of the International Conference of the IEEE Engineering in Medecine and Biology Society (EMBC), 2021.
[pdf]
 (2021) T. Gnassounou, P. Humbert, L. Oudre.
Adaptive subsampling of multidomain signals with graph products
In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021.
[conference]
[pdf]
[code]
 (2020) B. Le Bars, P. Humbert, A. Kalogeratos, N. Vayatis.
Learning the piecewise constant graph structure of a varying Ising model
In Proceedings of the 37th International Conference on Machine Learning (ICML), 2020.
[conference]
[pdf]
[code]
 (2020) P. Humbert, J. Audiffren, L. Oudre, N. Vayatis.
Low rank activations for tensorbased convolutional sparse coding
In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020.
[conference]
[pdf]
[code]
 (2019) P. Humbert, C. Dubost, J. Audiffren, L. Oudre.
Apprenticeship learning for a predictive state representation of anesthesia
IEEE Transactions on Biomedical Engineering (TBME), 67(7):20522063, 2020.
[journal]
[pdf]
 (2019) P. Humbert, L. Oudre, N. Vayatis.
Subsampling of multivariate timevertex graph signals
In Proceedings of the European Signal Processing Conference (EUSIPCO), 2019.
[conference]
 (2019) B. Le Bars*, P. Humbert*, L. Oudre, A. Kalogeratos.
Learning Laplacian matrix from bandlimited graph signals
In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.
[conference]
[pdf]
[code]