Tengyuan Liang. Estimating Certain Integral Probability Metric (IPM) Is as Hard as Estimating under the IPM. arXiv:1911.00730, 2019.
Tengyuan Liang. On the Minimax Optimality of Estimating the Wasserstein Metric. arXiv:1908.10324, 2019.
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai. On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. arXiv:1908.10292, 2019.
Xialiang Dou, Tengyuan Liang. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. arXiv:1901.07114, 2019.
Tengyuan Liang. On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. arXiv:1811.03179, 2019.
Max Farrell, Tengyuan Liang, Sanjog Misra. Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. arXiv:1809.09953, 2019.
Tengyuan Liang, Alexander Rakhlin. Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize. The Annals of Statistics, to appear, 2019.
Satyen Kale, Zohar Karnin, Tengyuan Liang, DÃ¡vid PÃ¡l. Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. International Conference on Machine Learning (ICML), 2017.
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin. Geometric Inference for General High-Dimensional Linear Inverse Problems. The Annals of Statistics, 2016.
T. Tony Cai, Tengyuan Liang, Harrison H. Zhou. Law of Log Determinant of Sample Covariance Matrix and Optimal Estimation of Differential Entropy for High-Dimensional Gaussian Distributions. Journal of Multivariate Analysis, 2015.