| 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 | 786 | 2018 |
| The generalization error of random features regression: Precise asymptotics and the double descent curve S Mei, A Montanari Communications on Pure and Applied Mathematics 75 (4), 667-766, 2022 | 520 | 2022 |
| The landscape of empirical risk for non-convex losses S Mei, Y Bai, A Montanari The Annals of Statistics 46 (6A), 2747-2774, 2018 | 323 | 2018 |
| 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 | 253 | 2019 |
| Linearized two-layers neural networks in high dimension B Ghorbani, S Mei, T Misiakiewicz, A Montanari The Annals of Statistics 49 (2), 1029-1054, 2021 | 204 | 2021 |
| When do neural networks outperform kernel methods? B Ghorbani, S Mei, T Misiakiewicz, A Montanari Advances in Neural Information Processing Systems 33, 14820-14830, 2020 | 145 | 2020 |
| 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 | 124 | 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 | 113 | 2019 |
| Generalization error of random feature and kernel methods: Hypercontractivity and kernel matrix concentration S Mei, T Misiakiewicz, A Montanari Applied and Computational Harmonic Analysis 59, 3-84, 2022 | 78 | 2022 |
| 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 | 74 | 2017 |
| When Can We Learn General-Sum Markov Games with a Large Number of Players Sample-Efficiently? Z Song, S Mei, Y Bai International Conference on Learning Representations (ICLR) 2022, 2021 | 67 | 2021 |
| Learning with invariances in random features and kernel models S Mei, T Misiakiewicz, A Montanari Conference on Learning Theory, 3351-3418, 2021 | 53 | 2021 |
| TAP free energy, spin glasses and variational inference Z Fan, S Mei, A Montanari The Annals of Probability 49 (1), 1-45, 2021 | 30 | 2021 |
| Don’t just blame over-parametrization for over-confidence: Theoretical analysis of calibration in binary classification Y Bai, S Mei, H Wang, C Xiong International Conference on Machine Learning, 566-576, 2021 | 28 | 2021 |
| Unified algorithms for rl with decision-estimation coefficients: No-regret, pac, and reward-free learning F Chen, S Mei, Y Bai arXiv preprint arXiv:2209.11745, 2022 | 23 | 2022 |
| Local convexity of the TAP free energy and AMP convergence for -synchronization M Celentano, Z Fan, S Mei The Annals of Statistics 51 (2), 519-546, 2023 | 22 | 2023 |
| Performance and limitations of the QAOA at constant levels on large sparse hypergraphs and spin glass models J Basso, D Gamarnik, S Mei, L Zhou 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 21 | 2022 |
| Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection Y Bai, F Chen, H Wang, C Xiong, S Mei arXiv preprint arXiv:2306.04637, 2023 | 16 | 2023 |
| Exact gap between generalization error and uniform convergence in random feature models Z Yang, Y Bai, S Mei International Conference on Machine Learning, 11704-11715, 2021 | 16 | 2021 |
| Near-optimal learning of extensive-form games with imperfect information Y Bai, C Jin, S Mei, T Yu International Conference on Machine Learning, 1337-1382, 2022 | 15 | 2022 |
Andrea MontanariProfessor of Statistics and Mathematics, Stanford UniversityVerified email at stanford.edu
