| Statistical optimality of stochastic gradient descent on hard learning problems through multiple passes L Pillaud-Vivien, A Rudi, F Bach Advances in Neural Information Processing Systems 31, 2018 | 87 | 2018 |
| Implicit bias of sgd for diagonal linear networks: a provable benefit of stochasticity S Pesme, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 34, 2021 | 60 | 2021 |
| Exponential convergence of testing error for stochastic gradient methods L Pillaud-Vivien, A Rudi, F Bach Conference On Learning Theory, 2018 | 32 | 2018 |
| Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs E Boursier, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 35, 2022 | 29 | 2022 |
| Sgd with large step sizes learns sparse features M Andriushchenko, AV Varre, L Pillaud-Vivien, N Flammarion International Conference on Machine Learning, 2023 | 26 | 2023 |
| Last iterate convergence of SGD for Least-Squares in the Interpolation regime. AV Varre, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 34, 2021 | 25 | 2021 |
| Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation L Pillaud-Vivien, J Reygner, N Flammarion Conference on Learning Theory, 2022 | 17 | 2022 |
| Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning V Cabannes, L Pillaud-Vivien, F Bach, A Rudi Advances in Neural Information Processing Systems 34, 2021 | 17 | 2021 |
| Statistical estimation of the poincaré constant and application to sampling multimodal distributions L Pillaud-Vivien, F Bach, T Lelièvre, A Rudi, G Stoltz International Conference on Artificial Intelligence and Statistics, 2020 | 13 | 2020 |
| Central Limit Theorem for stationary Fleming--Viot particle systems in finite spaces T Lelievre, L Pillaud-Vivien, J Reygner ALEA, Lat. Am. J. Probab. Math. Stat. 15, 1163–1182, 2018 | 12 | 2018 |
| On Single Index Models beyond Gaussian Data J Bruna, L Pillaud-Vivien, A Zweig Advances in Neural Information Processing Systems 37, 2023 | 5 | 2023 |
| Kernelized Diffusion maps L Pillaud-Vivien, F Bach Conference On Learning Theory, 2023 | 2 | 2023 |
| Learning with reproducing kernel Hilbert spaces: stochastic gradient descent and laplacian estimation L Pillaud-Vivien Université Paris sciences et lettres, 2020 | 2 | 2020 |
| On the spectral bias of two-layer linear networks AV Varre, ML Vladarean, L Pillaud-Vivien, N Flammarion Advances in Neural Information Processing Systems 37, 2023 | | 2023 |
| On Learning Gaussian Multi-index Models with Gradient Flow A Bietti, J Bruna, L Pillaud-Vivien arXiv preprint arXiv:2310.19793, 2023 | | 2023 |
| La Résilience à Paris: états des lieux et préconisations multi-bénéfices pour l’espace public A Hatchuel, A Labourdette, F Leduc, L Pillaud-Vivien, M Renaudin Mairie de Paris, 2017 | | 2017 |
Nicolas FlammarionEPFLVerified email at epfl.ch
