| Just interpolate: Kernel “ridgeless” regression can generalize T Liang, A Rakhlin Annals of Statistics 48 (3), 1329--1347, 2020 | 115 | 2020 |
| 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 | 107 | 2019 |
| 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 | 71 | 2019 |
| 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 | 62 | 2015 |
| 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 | 54 | 2015 |
| 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 | 51 | 2018 |
| 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 | 47 | 2017 |
| Learning with square loss: Localization through offset rademacher complexity T Liang, A Rakhlin, K Sridharan Conference on Learning Theory 40, 1260--1285, 2015 | 41 | 2015 |
| 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 | 25 | 2018 |
| On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results T Liang arXiv preprint arXiv:1811.03179, 2018 | 25 | 2018 |
| Inference via message passing on partially labeled stochastic block models TT Cai, T Liang, A Rakhlin arXiv preprint arXiv:1603.06923, 2016 | 25 | 2016 |
| Geometric inference for general high-dimensional linear inverse problems TT Cai, T Liang, A Rakhlin Annals of Statistics 44 (4), 1536--1563, 2016 | 22 | 2016 |
| How well can generative adversarial networks learn densities: A nonparametric view T Liang arXiv preprint arXiv:1712.08244, 2017 | 21 | 2017 |
| Deep neural networks for estimation and inference MH Farrell, T Liang, S Misra Econometrica, forthcoming, 2020 | 20 | 2020 |
| 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 | 19 | 2019 |
| 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 | 17 | 2020 |
| 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 | 14 | 2017 |
| 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 | 10 | 2019 |
