Neural Network/Deep Learning
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.
Tengyuan Liang, James Stokes. Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.