Pierre Humbert

Postdoc in probability and statistics

 

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 Paris-Saclay entitled Multivariate analysis with tensors and graphs - application to neuroscience, under the supervision of Nicolas VayatisLaurent Oudre, and Julien Audiffren.

 

CV is available here .

 

Main research interests

  • Statistical learning, non-parametric statistics
  • Cross-validation, resampling, bootstrap
  • Robust statistics
  • Signal processing
  • Graph and tensor learning
  • Applications to neuroscience

Contact

Département de Mathématiques
Bâtiment 307
Faculté des Sciences d'Orsay
Université Paris-Saclay
91405 Orsay Cedex
France

Office : 2F3

+33 1 69 15 60 28

prenom.nom@universite-paris-saclay.fr

Main publications

Journals

  • (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):1-47, 2021. [journal] [pdf] [code]

  • (2021) P. Humbert, L. Oudre, N. Vayatis, J. Audiffren. Tensor convolutional dictionary learning with CP low-rank activations
    IEEE Transactions on Signal Processing (TSP), 2021. [journal] [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):2052-2063, 2020. [journal] [pdf]

 

Conferences

  • (2022) P. Humbert*, B. Le Bars*, L. Minvielle*. Robust kernel density estimation with median-of-means principle
    In Proceedings of the 39th International Conference on Machine Learning (ICML), 2022. [conference] [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 piece-wise 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 tensor-based convolutional sparse coding
    In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2020. [conference] [pdf] [code]

  • (2019) P. Humbert, L. Oudre, N. Vayatis. Subsampling of multivariate time-vertex 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]