Maxime Sangnier
Maxime Sangnier
Sorbonne University
Zweryfikowany adres z upmc.fr - Strona główna
Cytowane przez
Cytowane przez
Some theoretical properties of GANs
G Biau, B Cadre, M Sangnier, U Tanielian
Joint quantile regression in vector-valued RKHSs
M Sangnier, O Fercoq, F d'Alché-Buc
Advances in Neural Information Processing Systems 29, 2016
Some theoretical insights into Wasserstein GANs
GÊ Biau, M Sangnier, U Tanielian
Journal of Machine Learning Research 22 (119), 1-45, 2021
Approximating Lipschitz continuous functions with GroupSort neural networks
U Tanielian, G Biau
International Conference on Artificial Intelligence and Statistics, 442-450, 2021
Maximum likelihood estimation for Hawkes processes with self-excitation or inhibition
A Bonnet, MM Herrera, M Sangnier
Statistics & Probability Letters 179, 109214, 2021
Inference of multivariate exponential Hawkes processes with inhibition and application to neuronal activity
A Bonnet, M Martinez Herrera, M Sangnier
Statistics and Computing 33 (4), 91, 2023
Infinite task learning in RKHSs
R Brault, A Lambert, Z Szabó, M Sangnier, F d’Alché-Buc
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
Reduced basis’ acquisition by a learning process for rapid on-line approximation of solution to pde’s: Laminar flow past a backstep
P Gallinari, Y Maday, M Sangnier, O Schwander, T Taddei
Archives of Computational Methods in Engineering 25, 131-141, 2018
Data sparse nonparametric regression with ε-insensitive losses
M Sangnier, O Fercoq, F d’Alché-Buc
Asian Conference on Machine Learning, 192-207, 2017
Filter bank learning for signal classification
M Sangnier, J Gauthier, A Rakotomamonjy
Signal Processing 113, 124-137, 2015
Output fisher embedding regression
M Djerrab, A Garcia, M Sangnier, F d’Alché-Buc
Machine Learning 107, 1229-1256, 2018
Early and reliable event detection using proximity space representation
M Sangnier, J Gauthier, A Rakotomamonjy
International Conference on Machine Learning, 2310-2319, 2016
Early frame-based detection of acoustic scenes
M Sangnier, J Gauthier, A Rakotomamonjy
IEEE International Workshop on Applications of Signal Processing to Audio …, 2015
Accelerated proximal boosting
E Fouillen, C Boyer, M Sangnier
arXiv preprint arXiv:1808.09670 134, 1073-1092, 2018
Comparaison de descripteurs pour la classification de décompositions parcimonieuses invariantes par translation
Q Barthélemy, M Sangnier, A Larue, JI Mars
GRETSI 2013-XXIVème Colloque francophone de traitement du signal et des …, 2013
Filter bank kernel learning for nonstationary signal classification
M Sangnier, J Gauthier, A Rakotomamonjy
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
Kernel learning as minimization of the single validation estimate
M Sangnier, J Gauthier, A Rakotomamonjy
IEEE Machine Learning for Signal Processing (MLSP), 2014 International …, 2014
Spectral analysis for noisy Hawkes processes inference
A Bonnet, F Cheysson, MM Herrera, M Sangnier
arXiv preprint arXiv:2405.12581, 2024
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
E Fouillen, C Boyer, M Sangnier
Neurocomputing 520, 301-319, 2023
Introduction to machine learning
M Sangnier
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