Tengyuan Liang

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 33

Top Topics

Top Venues

J. Mach. Learn. Res.

Publications

Kulunu Dharmakeerthi, YoonHaeng Hur, Tengyuan Liang
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction. CoRR (2024)
YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang
Reversible Gromov-Monge Sampler for Simulation-Based Inference. SIAM J. Math. Data Sci. 6 (2) (2024)
Tengyuan Liang, Benjamin Recht
Interpolating Classifiers Make Few Mistakes. J. Mach. Learn. Res. 24 (2023)
Tengyuan Liang
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria. CoRR (2022)
Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Chris Ryan
Online Learning to Transport via the Minimal Selection Principle. COLT (2022)
Tengyuan Liang, Subhabrata Sen, Pragya Sur
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. CoRR (2022)
Wenxuan Guo, YoonHaeng Hur, Tengyuan Liang, Christopher Ryan
Online Learning to Transport via the Minimal Selection Principle. CoRR (2022)
YoonHaeng Hur, Wenxuan Guo, Tengyuan Liang
Reversible Gromov-Monge Sampler for Simulation-Based Inference. CoRR (2021)
Tengyuan Liang
Universal Prediction Band via Semi-Definite Programming. CoRR (2021)
Tengyuan Liang, Benjamin Recht
Interpolating Classifiers Make Few Mistakes. CoRR (2021)
Tengyuan Liang
How Well Generative Adversarial Networks Learn Distributions. J. Mach. Learn. Res. 22 (2021)
Max H. Farrell, Tengyuan Liang, Sanjog Misra
Deep Learning for Individual Heterogeneity. CoRR (2020)
Tengyuan Liang, Hai Tran-Bach
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks. CoRR (2020)
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information. J. Mach. Learn. Res. 21 (2020)
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. COLT (2020)
Tengyuan Liang, Pragya Sur
A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers. CoRR (2020)
Tengyuan Liang, James Stokes
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. AISTATS (2019)
Xialiang Dou, Tengyuan Liang
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits. CoRR (2019)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. AISTATS (2019)
Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai
On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels. CoRR (2019)
Tengyuan Liang
On the Minimax Optimality of Estimating the Wasserstein Metric. CoRR (2019)
Belinda Tzen, Tengyuan Liang, Maxim Raginsky
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. COLT (2018)
Tengyuan Liang
On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results. CoRR (2018)
Tengyuan Liang, James Stokes
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks. CoRR (2018)
Tengyuan Liang, Alexander Rakhlin
Just Interpolate: Kernel "Ridgeless" Regression Can Generalize. CoRR (2018)
Max H. Farrell, Tengyuan Liang, Sanjog Misra
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands. CoRR (2018)
Belinda Tzen, Tengyuan Liang, Maxim Raginsky
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability. CoRR (2018)
Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. ICML (2017)
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
On Detection and Structural Reconstruction of Small-World Random Networks. IEEE Trans. Netw. Sci. Eng. 4 (3) (2017)
Tengyuan Liang
How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View. CoRR (2017)
Satyen Kale, Zohar S. Karnin, Tengyuan Liang, Dávid Pál
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP. CoRR (2017)
Tengyuan Liang, Tomaso A. Poggio, Alexander Rakhlin, James Stokes
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks. CoRR (2017)
T. Tony Cai, Tengyuan Liang, Alexander Rakhlin
On Detection and Structural Reconstruction of Small-World Random Networks. CoRR (2016)
Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. COLT (2015)
Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
Learning with Square Loss: Localization through Offset Rademacher Complexity. CoRR (2015)
Alexandre Belloni, Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions. CoRR (2015)
Tengyuan Liang, Alexander Rakhlin, Karthik Sridharan
Learning with Square Loss: Localization through Offset Rademacher Complexity. 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. J. Multivar. Anal. 137 (2015)
Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
On Zeroth-Order Stochastic Convex Optimization via Random Walks. CoRR (2014)
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. CoRR (2013)