热门出版物
标题/作者年份
Nonconvex optimization meets low-rank matrix factorization: An overview
Y Chi, YM Lu, Y Chen
IEEE Transactions on Signal Processing 67 (20), 5239-5269
2019
A modern maximum-likelihood theory for high-dimensional logistic regression
P Sur, EJ Candès
Proceedings of the National Academy of Sciences 116 (29), 14516-14525
2019
Implicit regularization in nonconvex statistical estimation: Gradient descent converges linearly for phase retrieval and matrix completion
C Ma, K Wang, Y Chi, Y Chen
International Conference on Machine Learning, 3345-3354
2018
High-dimensional asymptotics of prediction: Ridge regression and classification
E Dobriban, S Wager
The Annals of Statistics 46 (1), 247-279
2018
Gradient descent with random initialization: Fast global convergence for nonconvex phase retrieval
Y Chen, Y Chi, J Fan, C Ma
Mathematical Programming 176, 5-37
2019
Precise error analysis of regularized $ M $-estimators in high dimensions
C Thrampoulidis, E Abbasi, B Hassibi
IEEE Transactions on Information Theory 64 (8), 5592-5628
2018
The generalization error of max-margin linear classifiers: High-dimensional asymptotics in the overparametrized regime
A Montanari, F Ruan, Y Sohn, J Yan
arXiv preprint arXiv:1911.01544
2019
The likelihood ratio test in high-dimensional logistic regression is asymptotically a rescaled chi-square
P Sur, Y Chen, EJ Candès
Probability theory and related fields 175, 487-558
2019
Spectral method and regularized MLE are both optimal for top-K ranking
Y Chen, J Fan, C Ma, K Wang
Annals of statistics 47 (4), 2204
2019
A model of double descent for high-dimensional binary linear classification
Z Deng, A Kammoun, C Thrampoulidis
Information and Inference: A Journal of the IMA 11 (2), 435-495
2022
Breaking the sample size barrier in model-based reinforcement learning with a generative model
G Li, Y Wei, Y Chi, Y Gu, Y Chen
Advances in neural information processing systems 33, 12861-12872
2020
Inference and uncertainty quantification for noisy matrix completion
Y Chen, J Fan, C Ma, Y Yan
Proceedings of the National Academy of Sciences 116 (46), 22931-22937
2019
Spectral methods for data science: A statistical perspective
Y Chen, Y Chi, J Fan, C Ma
Foundations and Trends® in Machine Learning 14 (5), 566-806
2021
Nonconvex low-rank tensor completion from noisy data
C Cai, G Li, HV Poor, Y Chen
Advances in neural information processing systems 32
2019
The distribution of the lasso: Uniform control over sparse balls and adaptive parameter tuning
L Miolane, A Montanari
The Annals of Statistics 49 (4), 2313-2335
2021
Universality laws for high-dimensional learning with random features
H Hu, YM Lu
IEEE Transactions on Information Theory 69 (3), 1932-1964
2022
A precise high-dimensional asymptotic theory for boosting and minimum-ℓ 1-norm interpolated classifiers
T Liang, P Sur
The Annals of Statistics 50 (3), 1669-1695
2022
The lasso with general gaussian designs with applications to hypothesis testing
M Celentano, A Montanari, Y Wei
arXiv preprint arXiv:2007.13716
2020
Noisy matrix completion: Understanding statistical guarantees for convex relaxation via nonconvex optimization
Y Chen, Y Chi, J Fan, C Ma, Y Yan
SIAM journal on optimization 30 (4), 3098-3121
2020
Nonconvex matrix factorization from rank-one measurements
Y Li, C Ma, Y Chen, Y Chi
The 22nd International Conference on Artificial Intelligence and Statistics …
2019
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