Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance

Minimum distance estimation (MDE) gained recent attention as a formulation of (implicit) generative modeling. It considers minimizing, over model parameters, a statistical distance between the empirical data distribut ion and the model. This formulation lends itself well to theoretical analysis, but typ ical results are hindered by the curse of dimensionality.