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, 3693-3701, 2016
192016
Some theoretical properties of GANs
G Biau, B Cadre, M Sangnier, U Tanielian
Annals of Statistics 48 (3), 1539-1566, 2020
122020
Filter bank learning for signal classification
M Sangnier, J Gauthier, A Rakotomamonjy
Signal Processing 113, 124-137, 2015
72015
Output Fisher embedding regression
M Djerrab, A Garcia, M Sangnier, F d’Alché-Buc
Machine Learning 107 (8-10), 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
Data sparse nonparametric regression with ε-insensitive losses
M Sangnier, O Fercoq, F d’Alché-Buc
Asian Conference on Machine Learning, 192-207, 2017
42017
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
32019
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
32018
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
Accelerated proximal boosting
E Fouillen, C Boyer, M Sangnier
arXiv preprint arXiv:1808.09670, 2018
22018
Comparaison de descripteurs pour la classification de décompositions parcimonieuses invariantes par translation
Q Barthélemy, M Sangnier, A Larue, J Mars
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
Approximating Lipschitz continuous functions with GroupSort neural networks
U Tanielian, M Sangnier, G Biau
arXiv preprint arXiv:2006.05254, 2020
2020
Some Theoretical Insights into Wasserstein GANs
G Biau, M Sangnier, U Tanielian
arXiv preprint arXiv:2006.02682, 2020
2020
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
Proximal boosting and its acceleration
E Fouillen, C Boyer, M Sangnier
arXiv, arXiv: 1808.09670, 2018
2018
Infinite-Task Learning with Vector-Valued RKHSs
R Brault, A Lambert, Z Szabo, M Sangnier, F d'Alché-Buc
2018
Outils d'apprentissage automatique pour la reconnaissance de signaux temporels
M Sangnier
2015
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