Publications

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Tengyuan Liang, Benjamin Recht. Interpolating Classifiers Make Few Mistakes. Journal of Machine Learning Research, 2023.

Tengyuan Liang. Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. arXiv:2212.02457, 2022.

Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan. Online Learning to Transport via the Minimal Selection Principle. Conference on Learning Theory (COLT), 2022.

Tengyuan Liang. Universal Prediction Band via Semi-Definite Programming. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2022.

Tengyuan Liang, Subhabrata Sen, Pragya Sur. High-Dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. arXiv:2204.04476, 2022.

Tengyuan Liang, Pragya Sur. A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-L1-Norm Interpolated Classifiers. Annals of Statistics, 2022.

Tengyuan Liang, Hai Tran-Bach. Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. Journal of the American Statistical Association (Theory and Methods), 2022.

YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang. Reversible Gromov-Monge Sampler for Simulation-Based Inference. arXiv:2109.14090, 2021.

Tengyuan Liang. How Well Generative Adversarial Networks Learn Distributions. Journal of Machine Learning Research, 2021.

Max Farrell, Tengyuan Liang, Sanjog Misra. Deep Neural Networks for Estimation and Inference. Econometrica, 2021.

Preprint PDF Media

Xialiang Dou, Tengyuan Liang. Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. Journal of the American Statistical Association (Theory and Methods), 2021.

Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai. On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. Conference on Learning Theory (COLT), 2020.

Preprint PDF Corrigendum

Tengyuan Liang, Alexander Rakhlin. Just Interpolate: Kernel ''Ridgeless'' Regression Can Generalize. Annals of Statistics, 2020.

Preprint PDF Supplement

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, 2020.

Max Farrell, Tengyuan Liang, Sanjog Misra. Deep Learning for Individual Heterogeneity: An Automatic Inference Framework. arXiv:2010.14694, 2020.

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

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.

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.

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.

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

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.

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

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.

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

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

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.

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.