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

Blessings and curses of covariate shifts, directional convergece, and the connection to experimental design.

December 2022 · Tengyuan Liang

Interpolating Classifiers Make Few Mistakes

This paper provides elementary analyses of the regret and generalization of minimum-norm interpolating classifiers.

January 2021 · Tengyuan Liang, Benjamin Recht

How Well Generative Adversarial Networks Learn Distributions

This paper studies the rates of convergence for learning distributions implicitly with the adversarial framework and Generative Adversarial Networks (GANs), which subsume Wasserstein, Sobolev, MMD GAN, and Generalized/Simulated Method of Moments (GMM/SMM) as special cases. We study a wide range of parametric and nonparametric target distributions under a host of objective evaluation metrics. We investigate how to obtain valid statistical guarantees for GANs through the lens of regularization.

December 2017 · Tengyuan Liang