| Some theoretical properties of GANs G Biau, B Cadre, M Sangnier, U Tanielian | 57 | 2020 |
| Joint quantile regression in vector-valued RKHSs M Sangnier, O Fercoq, F d'Alché-Buc Advances in Neural Information Processing Systems 29, 2016 | 56 | 2016 |
| Some theoretical insights into Wasserstein GANs GÊ Biau, M Sangnier, U Tanielian Journal of Machine Learning Research 22 (119), 1-45, 2021 | 51 | 2021 |
| Approximating Lipschitz continuous functions with GroupSort neural networks U Tanielian, G Biau International Conference on Artificial Intelligence and Statistics, 442-450, 2021 | 38 | 2021 |
| Maximum likelihood estimation for Hawkes processes with self-excitation or inhibition A Bonnet, MM Herrera, M Sangnier Statistics & Probability Letters 179, 109214, 2021 | 31 | 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 | 20 | 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 | 15 | 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 | 9 | 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 | 9 | 2017 |
| Filter bank learning for signal classification M Sangnier, J Gauthier, A Rakotomamonjy Signal Processing 113, 124-137, 2015 | 8 | 2015 |
| Early and reliable event detection using proximity space representation M Sangnier, J Gauthier, A Rakotomamonjy International Conference on Machine Learning, 2310-2319, 2016 | 6 | 2016 |
| Output fisher embedding regression M Djerrab, A Garcia, M Sangnier, F d’Alché-Buc Machine Learning 107, 1229-1256, 2018 | 5 | 2018 |
| Early frame-based detection of acoustic scenes M Sangnier, J Gauthier, A Rakotomamonjy IEEE International Workshop on Applications of Signal Processing to Audio …, 2015 | 4 | 2015 |
| Accelerated proximal boosting E Fouillen, C Boyer, M Sangnier arXiv preprint arXiv:1808.09670 134, 1073-1092, 2018 | 2 | 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 | 2 | 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 | 2 | 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 | 1 | 2014 |
| Spectral analysis for noisy Hawkes processes inference A Bonnet, F Cheysson, MM Herrera, M Sangnier arXiv preprint arXiv:2405.12581, 2024 | | 2024 |
| Proximal boosting: aggregating weak learners to minimize non-differentiable losses E Fouillen, C Boyer, M Sangnier Neurocomputing 520, 301-319, 2023 | | 2023 |
| Introduction to machine learning M Sangnier | | 2021 |
Florence d'Alché-BucTélécom Paris, Institut Polytechnique de ParisVerified email at telecom-paris.fr
