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Tengyuan Liang
Tengyuan Liang
Verified email at chicagobooth.edu - Homepage
Title
Cited by
Cited by
Year
Deep neural networks for estimation and inference
MH Farrell, T Liang, S Misra
Econometrica 89 (1), 181-213, 2021
614*2021
Just interpolate: Kernel “ridgeless” regression can generalize
T Liang, A Rakhlin
Annals of Statistics 48 (3), 1329--1347, 2020
3472020
Fisher-rao metric, geometry, and complexity of neural networks
T Liang, T Poggio, A Rakhlin, J Stokes
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
2312019
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
T Liang, J Stokes
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
2092019
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels
T Liang, A Rakhlin, X Zhai
Conference on Learning Theory 125, 2683--2711, 2020
135*2020
How well generative adversarial networks learn distributions
T Liang
Journal of Machine Learning Research 22 (228), 1-41, 2021
114*2021
Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions
TT Cai, T Liang, HH Zhou
Journal of Multivariate Analysis 137, 161--172, 2015
902015
Escaping the local minima via simulated annealing: Optimization of approximately convex functions
A Belloni, T Liang, H Narayanan, A Rakhlin
Conference on Learning Theory 40, 240--265, 2015
882015
A precise high-dimensional asymptotic theory for Boosting and minimum-ℓ1-norm interpolated classifiers
T Liang, P Sur
The Annals of Statistics 50 (3), 1669-1695, 2022
802022
Learning with square loss: Localization through offset rademacher complexity
T Liang, A Rakhlin, K Sridharan
Conference on Learning Theory 40, 1260--1285, 2015
772015
Computational and statistical boundaries for submatrix localization in a large noisy matrix
TT Cai, T Liang, A Rakhlin
Annals of Statistics 45 (4), 1403--1430, 2017
712017
Training neural networks as learning data-adaptive kernels: Provable representation and approximation benefits
X Dou, T Liang
Journal of the American Statistical Association 116 (535), 1507-1520, 2021
512021
Textual Factors: A Scalable, Interpretable, and Data-driven Approach to Analyzing Unstructured Information
LW Cong, T Liang, X Zhang
SSRN: https://ssrn.com/abstract=3307057, 2019
502019
On how well generative adversarial networks learn densities: Nonparametric and parametric results
T Liang
arXiv 2018, 2018
412018
Deep learning for individual heterogeneity: an automatic inference framework
MH Farrell, T Liang, S Misra
arXiv preprint arXiv:2010.14694, 2020
392020
Weighted message passing and minimum energy flow for heterogeneous stochastic block models with side information
TT Cai, T Liang, A Rakhlin
Journal of Machine Learning Research 21 (11), 1--34, 2020
36*2020
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability
B Tzen, T Liang, M Raginsky
Conference on Learning Theory 75, 857--875, 2018
352018
Interpolating Classifiers Make Few Mistakes
T Liang, B Recht
Journal of Machine Learning Research 24 (20), 1−27, 2023
312023
Geometric inference for general high-dimensional linear inverse problems
TT Cai, T Liang, A Rakhlin
Annals of Statistics 44 (4), 1536--1563, 2016
312016
Estimating certain integral probability metric (IPM) is as hard as estimating under the IPM
T Liang
arXiv preprint arXiv:1911.00730, 2019
23*2019
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Articles 1–20