| Robust statistical learning with Lipschitz and convex loss functions G Chinot, G Lecué, M Lerasle Probability Theory and related fields 176 (3), 897-940, 2020 | 52* | 2020 |
| Gradient descent can learn less over-parameterized two-layer neural networks on classification problems A Nitanda, G Chinot, T Suzuki arXiv preprint arXiv:1905.09870, 2019 | 52* | 2019 |
| On the robustness of the minimum interpolator G Chinot, M Lerasle arXiv preprint arXiv:2003.05838, 2020 | 31* | 2020 |
| On the robustness of minimum norm interpolators and regularized empirical risk minimizers G Chinot, M Löffler, S van de Geer The Annals of Statistics 50 (4), 2306-2333, 2022 | 28* | 2022 |
| Robust high dimensional learning for Lipschitz and convex losses C Geoffrey, L Guillaume, L Matthieu Journal of Machine Learning Research 21 (233), 1-47, 2020 | 21 | 2020 |
| ERM and RERM are optimal estimators for regression problems when malicious outliers corrupt the labels G Chinot arXiv preprint arXiv:1910.10923, 2019 | 9 | 2019 |
| AdaBoost and robust one-bit compressed sensing G Chinot, F Kuchelmeister, M Löffler, S van de Geer Mathematical Statistics and Learning 5 (1), 117-158, 2022 | 7 | 2022 |
| Robust learning and complexity dependent bounds for regularized problems G Chinot arXiv preprint arXiv:1902.02238, 2019 | 3 | 2019 |
| Minimum ℓ1 norm interpolation via basis pursuit is robust to errors G Chinot, M Löffler, S van de Geer arXiv preprint arXiv:2012.00807, 2020 | 2 | 2020 |
| Localization methods with applications to robust learning and interpolation G Chinot Institut Polytechnique de Paris, 2020 | | 2020 |
Matthieu LerasleEnsae-CRESTVerified email at ensae.fr
Matthieu LerasleEnsae-CRESTVerified email at ensae.fr