Tengyuan Liang

Assistant Professor of Econometrics and Statistics

Biography

Dr. Liang is an assistant professor at Chicago Booth. He is also the George C. Tiao faculty fellow in data science research.

His current research interests include computational and algorithmic aspects of statistical inference, machine learning and statistical learning theory, stochastic methods in non-convex optimization.

Interests

Data Science
Statistical Inference
Statistical Learning
Stochastic Optimization
Economic and Business Applications

Education

PhD in Statistics, 2017

Wharton School at University of Pennsylvania
BSc in Mathematics and Applied Mathematics, 2012

Peking University

Publications

Tengyuan Liang. Estimating Certain Integral Probability Metric (IPM) Is as Hard as Estimating under the IPM. arXiv:1911.00730, 2019.

Preprint PDF Project Project

Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai. On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. arXiv:1908.10292, 2019.

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Xialiang Dou, Tengyuan Liang. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. arXiv:1901.07114, 2019.

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Tengyuan Liang. On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. arXiv:1811.03179, 2019.

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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.

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Tengyuan Liang, Alexander Rakhlin. Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize. The Annals of Statistics, to appear, 2019.

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Tengyuan Liang, Weijie Su. Statistical Inference for the Population Landscape via Moment Adjusted Stochastic Gradients. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2019.

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T. Tony Cai, Tengyuan Liang, Alexander Rakhlin. Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. Journal of Machine Learning Research, to appear, 2019.

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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.

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Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes. Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

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Belinda Tzen, Tengyuan Liang, Maxim Raginsky. Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. Conference on Learning Theory (COLT), 2018.

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Satyen Kale, Zohar Karnin, Tengyuan Liang, Dávid Pál. Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. International Conference on Machine Learning (ICML), 2017.

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T. Tony Cai, Tengyuan Liang, Alexander Rakhlin. Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix. The Annals of Statistics, 2017.

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T. Tony Cai, Tengyuan Liang, Alexander Rakhlin. On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Transactions on Network Science and Engineering, 2017.

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T. Tony Cai, Tengyuan Liang, Alexander Rakhlin. Geometric Inference for General High-Dimensional Linear Inverse Problems. The Annals of Statistics, 2016.

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Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan. Learning with Square Loss: Localization through Offset Rademacher Complexity. Conference on Learning Theory (COLT), 2015.

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Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin. Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. Conference on Learning Theory (COLT), 2015.

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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.

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