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Song Mei
Song Mei
Assistant Professor at UC Berkeley
Verified email at berkeley.edu - Homepage
Title
Cited by
Cited by
Year
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
8832018
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
6022022
The landscape of empirical risk for non-convex losses
S Mei, Y Bai, A Montanari
The Annals of Statistics 46 (6A), 2747-2774, 2018
3482018
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
2792019
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
2302021
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
1782020
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
1402019
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
1222019
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
1082022
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
912021
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
762017
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
652024
Learning with invariances in random features and kernel models
S Mei, T Misiakiewicz, A Montanari
Conference on Learning Theory, 3351-3418, 2021
652021
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
432021
TAP free energy, spin glasses and variational inference
Z Fan, S Mei, A Montanari
The Annals of Probability 49 (1), 1-45, 2021
342021
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
302023
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
302022
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
272022
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
212022
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
182021
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