Tengyuan Liang

发表

Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP

Tengyuan Liang, Satyen Kale, Dávid Pál, 2017, ICML.

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

Tomaso A. Poggio, Tengyuan Liang, Alexander Rakhlin, 2017, AISTATS.

Geometrizing Local Rates of Convergence for Linear Inverse Problems

T. T. Cai, Tengyuan Liang, A. Rakhlin, 2014 .

Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix

Tengyuan Liang, A. Rakhlin, T. Cai, 2015, 1502.01988.

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability pdf

Maxim Raginsky, Tengyuan Liang, Belinda Tzen, 2018, COLT.

A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers

Tengyuan Liang, P. Sur, Tengyuan Liang, 2020, ArXiv.

Analyzing Textual Information at Scale

Tengyuan Liang, Lin William Cong, Baozhong Yang, 2019, SSRN Electronic Journal.

Law of log determinant of sample covariance matrix and optimal estimation of differential entropy for high-dimensional Gaussian distributions

Tengyuan Liang, T. Tony Cai, Harrison H. Zhou, 2013, Journal of Multivariate Analysis.

Inference via Message Passing on Partially Labeled Stochastic Block Models

Tengyuan Liang, A. Rakhlin, T. Cai, 2016, 1603.06923.

How Well Generative Adversarial Networks Learn Distributions

Tengyuan Liang, Tengyuan Liang, 2018, J. Mach. Learn. Res..

Estimating Certain Integral Probability Metrics (IPMs) Is as Hard as Estimating under the IPMs

Tengyuan Liang, 2019, SSRN Electronic Journal.

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize pdf

Tengyuan Liang, Alexander Rakhlin, Tengyuan Liang, 2018, The Annals of Statistics.

Escaping the Local Minima via Simulated Annealing: Optimization of Approximately Convex Functions

Hariharan Narayanan, Tengyuan Liang, Alexander Rakhlin, 2015, COLT.

Forthcoming in Econometrica DEEP NEURAL NETWORKS FOR ESTIMATION AND INFERENCE1

Tengyuan Liang, M. Farrell, S. Misra, 2020 .

HOW WELL GENERATIVE ADVERSARIAL NETWORKS LEARN DISTRIBUTIONS1

Tengyuan Liang, 2020 .

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

Tengyuan Liang, James Stokes, Tengyuan Liang, 2018, AISTATS.

Geometric Inference for General High-Dimensional Linear Inverse Problems

Tengyuan Liang, A. Rakhlin, T. Cai, 2014, 1404.4408.

Reversible Gromov-Monge Sampler for Simulation-Based Inference

Tengyuan Liang, Y. Hur, Wenxuan Guo, 2021, ArXiv.

Online Learning to Transport via the Minimal Selection Principle

Tengyuan Liang, Wenxuan Guo, Y. Hur, 2022, COLT.

Universal prediction band via semi‐definite programming

Tengyuan Liang, 2021, SSRN Electronic Journal.

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

Tengyuan Liang, 2022, ArXiv.

Online Learning to Transport via the Minimal Selection Principle

Tengyuan Liang, Wenxuan Guo, Y. Hur, 2022, COLT.

Deep Neural Networks for Estimation and Inference

Sanjog Misra, Max H. Farrell, Tengyuan Liang, 2018, Econometrica.

Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits pdf

Tengyuan Liang, Xialiang Dou, Tengyuan Liang, 2019, Journal of the American Statistical Association.

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands pdf

Sanjog Misra, Max H. Farrell, Tengyuan Liang, 2018, Econometrica.

How Well Can Generative Adversarial Networks (GAN) Learn Densities: A Nonparametric View pdf

Tengyuan Liang, Tengyuan Liang, 2017, ArXiv.

Deep Learning for Individual Heterogeneity

Sanjog Misra, Max H. Farrell, Tengyuan Liang, 2020, ArXiv.

Statistical inference for the population landscape via moment‐adjusted stochastic gradients

Tengyuan Liang, Weijie Su, Weijie J. Su, 2017, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

On How Well Generative Adversarial Networks Learn Densities: Nonparametric and Parametric Results pdf

Tengyuan Liang, Tengyuan Liang, 2018, ArXiv.

Geometrizing Local Rates of Convergence for High-Dimensional Linear Inverse Problems

Tengyuan Liang, A. Rakhlin, T. Cai, 2014 .

Learning with Square Loss: Localization through Offset Rademacher Complexity pdf

Karthik Sridharan, Tengyuan Liang, Alexander Rakhlin, 2015, COLT.

Interpolating Classifiers Make Few Mistakes pdf

Benjamin Recht, Tengyuan Liang, B. Recht, 2021, J. Mach. Learn. Res..

On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels

Tengyuan Liang, Xiyu Zhai, Alexander Rakhlin, 2019, COLT.

Textual Factors: A Scalable, Interpretable, and Data-driven Approach to Analyzing Unstructured Information

Xiao Zhang, Tengyuan Liang, Lin William Cong, 2019 .

A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers pdf

Tengyuan Liang, Pragya Sur, Tengyuan Liang, 2020, SSRN Electronic Journal.

On Detection and Structural Reconstruction of Small-World Random Networks

Tengyuan Liang, Alexander Rakhlin, T. Tony Cai, 2016, IEEE Transactions on Network Science and Engineering.

Mehler’s Formula, Branching Process, and Compositional Kernels of Deep Neural Networks pdf

Tengyuan Liang, Hai Tran-Bach, Tengyuan Liang, 2020, Journal of the American Statistical Association.

High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models

Tengyuan Liang, P. Sur, Subhabrata Sen, 2022, Information and Inference: A Journal of the IMA.

Weak Signal Detection via Displacement Interpolation

Tengyuan Liang, Y. Hur, 2023, 2305.15609.

Deep Learning for Individual Heterogeneity pdf

Sanjog Misra, Max H. Farrell, Tengyuan Liang, 2020, ArXiv.

On the Risk of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels pdf

Tengyuan Liang, Alexander Rakhlin, Xiyu Zhai, 2019, ArXiv.

On Zeroth-Order Stochastic Convex Optimization via Random Walks pdf

Hariharan Narayanan, Tengyuan Liang, Alexander Rakhlin, 2014, ArXiv.

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria

Tengyuan Liang, 2022, ArXiv.

Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information

Tengyuan Liang, Alexander Rakhlin, T. Tony Cai, 2017, J. Mach. Learn. Res..

Reversible Gromov-Monge Sampler for Simulation-Based Inference

Tengyuan Liang, Y. Hur, Wenxuan Guo, 2021, ArXiv.