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
1014 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
695 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
376 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
319 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
271 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
211 2020 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
162 2024 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
158 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
145 2022 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
134 2019 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
113 2021 Learning with invariances in random features and kernel models S Mei, T Misiakiewicz, A Montanari
Conference on Learning Theory, 3351-3418, 2021
86 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
80 2017 Negative preference optimization: From catastrophic collapse to effective unlearning R Zhang, L Lin, Y Bai, S Mei
The First Conference on Language Modeling (COLM) 2024, 2024
57 2024 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
53 2021 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
International Conference on Learning Representations (ICLR) 2024, 2023
46 * 2023 TAP free energy, spin glasses and variational inference Z Fan, S Mei, A Montanari
The Annals of Probability 49 (1), 1-45, 2021
42 2021 Transformers as decision makers: Provable in-context reinforcement learning via supervised pretraining L Lin, Y Bai, S Mei
International Conference on Learning Representations (ICLR) 2024, 2023
41 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
37 2022 Local convexity of the TAP free energy and AMP convergence for M Celentano, Z Fan, S Mei
The Annals of Statistics 51 (2), 519-546, 2023
36 2023 
Andrea MontanariProfessor of Statistics and Mathematics, Stanford UniversityE-mail confirmado em stanford.edu