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.
JMLR
Interpolating Classifiers Make Few Mistakes
This paper provides elementary analyses of the regret and generalization of minimum-norm interpolating classifiers.
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.