Tengyuan Liang
According to our database1,
Tengyuan Liang
authored at least 29 papers
between 2014 and 2023.
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Bibliography
2023
2022
Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria.
CoRR, 2022
CoRR, 2022
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022
2021
J. Mach. Learn. Res., 2021
2020
Weighted Message Passing and Minimum Energy Flow for Heterogeneous Stochastic Block Models with Side Information.
J. Mach. Learn. Res., 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
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
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