Tengyuan Liang

According to our database1, Tengyuan Liang authored at least 30 papers between 2014 and 2024.

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Bibliography

2024
Reversible Gromov-Monge Sampler for Simulation-Based Inference.
SIAM J. Math. Data Sci., 2024

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria.
J. Mach. Learn. Res., 2024

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction.
CoRR, 2024

2023
Interpolating Classifiers Make Few Mistakes.
J. Mach. Learn. Res., 2023

2022
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models.
CoRR, 2022

Online Learning to Transport via the Minimal Selection Principle.
CoRR, 2022

Online Learning to Transport via the Minimal Selection Principle.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
How Well Generative Adversarial Networks Learn Distributions.
J. Mach. Learn. Res., 2021

Universal Prediction Band via Semi-Definite Programming.
CoRR, 2021

2020
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information.
J. Mach. Learn. Res., 2020

Deep Learning for Individual Heterogeneity.
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
On the Minimax Optimality of Estimating the Wasserstein Metric.
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

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

Just Interpolate: Kernel "Ridgeless" Regression Can Generalize.
CoRR, 2018

Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability.
Proceedings of the Conference On Learning Theory, 2018

2017
On Detection and Structural Reconstruction of Small-World Random Networks.
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

Learning with Square Loss: Localization through Offset Rademacher Complexity.
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
On Zeroth-Order Stochastic Convex Optimization via Random Walks.
CoRR, 2014


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