| A mean field view of the landscape of two-layers neural networks S Mei, A Montanari, P Nguyen Proceedings of the National Academy of Sciences 115, E7665-E7671, 2018 | 303 | 2018 |
| The landscape of empirical risk for non-convex losses S Mei, Y Bai, A Montanari The Annals of Statistics 46 (6A), 2747-2774, 2018 | 181 | 2018 |
| The generalization error of random features regression: Precise asymptotics and double descent curve S Mei, A Montanari arXiv preprint arXiv:1908.05355, 2019 | 119 | 2019 |
| Linearized two-layers neural networks in high dimension B Ghorbani, S Mei, T Misiakiewicz, A Montanari arXiv preprint arXiv:1904.12191, 2019 | 78 | 2019 |
| Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit S Mei, T Misiakiewicz, A Montanari Conference on Learning Theory (COLT) 2019, 2019 | 78 | 2019 |
| The landscape of the spiked tensor model GB Arous, S Mei, A Montanari, M Nica Communications on Pure and Applied Mathematics 72 (11), 2282-2330, 2019 | 42 | 2019 |
| Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality S Mei, T Misiakiewicz, A Montanari, RI Oliveira Conference on Learning Theory (COLT) 2017, 2017 | 40 | 2017 |
| Limitations of Lazy Training of Two-layers Neural Network B Ghorbani, S Mei, T Misiakiewicz, A Montanari Advances in Neural Information Processing Systems, 9108-9118, 2019 | 29 | 2019 |
| When do neural networks outperform kernel methods? B Ghorbani, S Mei, T Misiakiewicz, A Montanari arXiv preprint arXiv:2006.13409, 2020 | 12 | 2020 |
| TAP free energy, spin glasses and variational inference Z Fan, S Mei, A Montanari Annals of Probability 49 (1), 1-45, 2021 | 7 | 2021 |
| On a molecular based Q-tensor model for liquid crystals with density variations S Mei, P Zhang Multiscale Modeling & Simulation 13 (3), 977-1000, 2015 | 3 | 2015 |
| Proximal algorithms for constrained composite optimization, with applications to solving low-rank sdps Y Bai, J Duchi, S Mei arXiv preprint arXiv:1903.00184, 2019 | 2 | 2019 |
| Analysis of sequential quadratic programming through the lens of Riemannian optimization Y Bai, S Mei arXiv preprint arXiv:1805.08756, 2018 | 1 | 2018 |
| Discussion of:“Nonparametric regression using deep neural networks with ReLU activation function” B Ghorbani, S Mei, T Misiakiewicz, A Montanari Annals of Statistics 48 (4), 1898-1901, 2020 | | 2020 |
Andrea MontanariProfessor of Electrical Engineering and Statistics, Stanford UniversityVerified email at stanford.edu
