Maxime Sangnier
Maxime Sangnier
Sorbonne University
Adresse e-mail validée de upmc.fr - Page d'accueil
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Joint quantile regression in vector-valued RKHSs
M Sangnier, O Fercoq, F d'Alché-Buc
Advances in Neural Information Processing Systems 29, 3693-3701, 2016
292016
Some theoretical properties of GANs
G Biau, B Cadre, M Sangnier, U Tanielian
arXiv preprint arXiv:1803.07819, 2018
242018
Filter bank learning for signal classification
M Sangnier, J Gauthier, A Rakotomamonjy
Signal Processing 113, 124-137, 2015
82015
Approximating Lipschitz continuous functions with GroupSort neural networks
U Tanielian, G Biau
International Conference on Artificial Intelligence and Statistics, 442-450, 2021
72021
Some theoretical insights into Wasserstein GANs
G Biau, M Sangnier, U Tanielian
Journal of Machine Learning Research, 2021
62021
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
62019
Output fisher embedding regression
M Djerrab, A Garcia, M Sangnier, F d’Alché-Buc
Machine Learning 107 (8), 1229-1256, 2018
62018
Early and reliable event detection using proximity space representation
M Sangnier, J Gauthier, A Rakotomamonjy
International Conference on Machine Learning, 2310-2319, 2016
62016
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 (1), 131-141, 2018
42018
Data sparse nonparametric regression with ε-insensitive losses
M Sangnier, O Fercoq, F d’Alché-Buc
Asian Conference on Machine Learning, 192-207, 2017
32017
Early frame-based detection of acoustic scenes
M Sangnier, J Gauthier, A Rakotomamonjy
IEEE International Workshop on Applications of Signal Processing to Audio …, 2015
32015
Proximal boosting and variants
E Fouillen, C Boyer, M Sangnier
22021
Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
A Bonnet, M Herrera, M Sangnier
arXiv preprint arXiv:2103.05299, 2021
22021
Comparaison de descripteurs pour la classification de décompositions parcimonieuses invariantes par translation
Q Barthélemy, M Sangnier, A Larue, J Mars
XXIVème colloque GRETSI (GRETSI 2013), ID414, 2013
22013
Filter bank kernel learning for nonstationary signal classification
M Sangnier, J Gauthier, A Rakotomamonjy
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
22013
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
12014
Introduction to machine learning
M Sangnier
2021
Maximum Likelihood Estimation for Hawkes Processes with self-excitation or inhibition
MM Herrera, A Bonnet, M Herrera, M Sangnier
2021
Some elements on convex optimization
M Sangnier
2020
Infinite Task Learning with Vector-Valued RKHSs
A Lambert, R Brault, Z Szabo, M Sangnier, F d’Alché-Buc
2018
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