Tengyuan Liang
Tengyuan Liang
University of Chicago, Booth School of Business
Geverifieerd e-mailadres voor chicagobooth.edu - Homepage
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Just interpolate: Kernel “ridgeless” regression can generalize
T Liang, A Rakhlin
Annals of Statistics 48 (3), 1329--1347, 2020
1152020
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
1072019
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
712019
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
622015
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
542015
Deep neural networks for estimation and inference: Application to causal effects and other semiparametric estimands
MH Farrell, T Liang, S Misra
arXiv preprint arXiv:1809.09953, 2018
512018
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
472017
Learning with square loss: Localization through offset rademacher complexity
T Liang, A Rakhlin, K Sridharan
Conference on Learning Theory 40, 1260--1285, 2015
412015
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
252018
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results
T Liang
arXiv preprint arXiv:1811.03179, 2018
252018
Inference via message passing on partially labeled stochastic block models
TT Cai, T Liang, A Rakhlin
arXiv preprint arXiv:1603.06923, 2016
252016
Geometric inference for general high-dimensional linear inverse problems
TT Cai, T Liang, A Rakhlin
Annals of Statistics 44 (4), 1536--1563, 2016
222016
How well can generative adversarial networks learn densities: A nonparametric view
T Liang
arXiv preprint arXiv:1712.08244, 2017
212017
Deep neural networks for estimation and inference
MH Farrell, T Liang, S Misra
Econometrica, forthcoming, 2020
202020
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
19*2020
On the risk of minimum-norm interpolants and restricted lower isometry of kernels
T Liang, A Rakhlin, X Zhai
arXiv preprint arXiv:1908.10292, 2019
192019
Training neural networks as learning data-adaptive kernels: Provable representation and approximation benefits
X Dou, T Liang
Journal of the American Statistical Association, 1--14, 2020
172020
Geometrizing local rates of convergence for high-dimensional linear inverse problems
TT Cai, T Liang, A Rakhlin
arXiv preprint arXiv:1404.4408, 2014
15*2014
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP
S Kale, Z Karnin, T Liang, D Pál
International Conference on Machine Learning 70, 1780--1788, 2017
142017
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
102019
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Artikelen 1–20