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 | 929 | 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 | 640 | 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 | 354 | 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 | 293 | 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 | 247 | 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 | 189 | 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 | 145 | 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 | 124 | 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 | 121 | 2022 |
Transformers as statisticians: Provable in-context learning with in-context algorithm selection Y Bai, F Chen, H Wang, C Xiong, S Mei Advances in neural information processing systems 36, 2024 | 101 | 2024 |
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 | 96 | 2021 |
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 | 77 | 2017 |
Learning with invariances in random features and kernel models S Mei, T Misiakiewicz, A Montanari Conference on Learning Theory, 3351-3418, 2021 | 70 | 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 | 46 | 2021 |
TAP free energy, spin glasses and variational inference Z Fan, S Mei, A Montanari The Annals of Probability 49 (1), 1-45, 2021 | 39 | 2021 |
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 | 35 | 2023 |
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 | 34 | 2022 |
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 | 30 | 2022 |
How do transformers learn in-context beyond simple functions? a case study on learning with representations T Guo, W Hu, S Mei, H Wang, C Xiong, S Savarese, Y Bai arXiv preprint arXiv:2310.10616, 2023 | 26 | 2023 |
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 | 24 | 2022 |