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 | 100 | 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 | 96 | 2021 |
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 | 53 | 2022 |
Sgd with large step sizes learns sparse features M Andriushchenko, AV Varre, L Pillaud-Vivien, N Flammarion International Conference on Machine Learning, 2023 | 46 | 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 | 35 | 2021 |
Exponential convergence of testing error for stochastic gradient methods L Pillaud-Vivien, A Rudi, F Bach Conference On Learning Theory, 2018 | 33 | 2018 |
Label noise (stochastic) gradient descent implicitly solves the Lasso for quadratic parametrisation L Pillaud-Vivien, J Reygner, N Flammarion Conference on Learning Theory, 2022 | 30 | 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 | 20 | 2021 |
On Learning Gaussian Multi-index Models with Gradient Flow A Bietti, J Bruna, L Pillaud-Vivien arXiv preprint arXiv:2310.19793, 2023 | 17 | 2023 |
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 | 15 | 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 | 14 | 2018 |
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 | 7 | 2023 |
On Single Index Models beyond Gaussian Data J Bruna, L Pillaud-Vivien, A Zweig Advances in Neural Information Processing Systems 37, 2023 | 7* | 2023 |
Computational-Statistical Gaps in Gaussian Single-Index Models A Damian, L Pillaud-Vivien, J Lee, J Bruna The Thirty Seventh Annual Conference on Learning Theory, 1262-1262, 2024 | 6* | 2024 |
Kernelized Diffusion maps L Pillaud-Vivien, F Bach Conference On Learning Theory, 2023 | 6 | 2023 |
Learning with reproducing kernel Hilbert spaces: stochastic gradient descent and laplacian estimation L Pillaud-Vivien Université Paris sciences et lettres, 2020 | 2 | 2020 |
Batch and match: black-box variational inference with a score-based divergence D Cai, C Modi, L Pillaud-Vivien, CC Margossian, RM Gower, DM Blei, ... arXiv preprint arXiv:2402.14758, 2024 | 1 | 2024 |
Stochastic Differential Equations models for Least-Squares Stochastic Gradient Descent A Schertzer, L Pillaud-Vivien arXiv preprint arXiv:2407.02322, 2024 | | 2024 |
An Ordering of Divergences for Variational Inference with Factorized Gaussian Approximations CC Margossian, L Pillaud-Vivien, LK Saul arXiv preprint arXiv:2403.13748, 2024 | | 2024 |
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 |