Song Mei
TitleCited byYear
The landscape of empirical risk for non-convex losses
S Mei, Y Bai, A Montanari
The Annals of Statistics 46 (6A), 2747-2774, 2018
912018
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
842018
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
222017
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
182019
The landscape of the spiked tensor model
GB Arous, S Mei, A Montanari, M Nica
arXiv preprint arXiv:1711.05424, 2017
182017
Linearized two-layers neural networks in high dimension
B Ghorbani, S Mei, T Misiakiewicz, A Montanari
arXiv preprint arXiv:1904.12191, 2019
122019
TAP free energy, spin glasses, and variational inference
Z Fan, S Mei, A Montanari
arXiv preprint arXiv:1808.07890, 2018
22018
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
22015
The generalization error of random features regression: Precise asymptotics and double descent curve
S Mei, A Montanari
arXiv preprint arXiv:1908.05355, 2019
2019
Limitations of lazy training of two-layers neural networks
B Ghorbani, S Mei, T Misiakiewicz, A Montanari
arXiv preprint arXiv:1906.08899, 2019
2019
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
2019
Analysis of sequential quadratic programming through the lens of Riemannian optimization
Y Bai, S Mei
arXiv preprint arXiv:1805.08756, 2018
2018
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Articles 1–12