# Tengyuan Liang

According to our database

Collaborative distances:

^{1}, Tengyuan Liang authored at least 30 papers between 2014 and 2024.Collaborative distances:

## Timeline

#### Legend:

Book In proceedings Article PhD thesis Dataset Other## Links

#### On csauthors.net:

## Bibliography

2024

SIAM J. Math. Data Sci., 2024

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction.

CoRR, 2024

2023

J. Mach. Learn. Res., 2023

2022

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

CoRR, 2022

CoRR, 2022

CoRR, 2022

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

J. Mach. Learn. Res., 2021

CoRR, 2021

2020

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

J. Mach. Learn. Res., 2020

CoRR, 2020

Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks.

CoRR, 2020

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

CoRR, 2020

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

Proceedings of the Conference on Learning Theory, 2020

2019

CoRR, 2019

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

CoRR, 2019

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

CoRR, 2019

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

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

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

CoRR, 2018

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

CoRR, 2018

CoRR, 2018

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

Proceedings of the Conference On Learning Theory, 2018

2017

IEEE Trans. Netw. Sci. Eng., 2017

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

CoRR, 2017

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

Proceedings of the 34th International Conference on Machine Learning, 2017

2015

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

J. Multivar. Anal., 2015

Proceedings of The 28th Conference on Learning Theory, 2015

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

Proceedings of The 28th Conference on Learning Theory, 2015

2014

CoRR, 2014