Marginal Tail-Adaptive Normalizing Flows

Understand

Learning the tail behavior of a distribution is a notoriously difficult problem.

• By definition, the number of samples from the tail is small, and deep generative models, such as normalizing flows, tend to concentrate on learning the body of the distribution.
• In this paper, we focus on improving the ability of normalizing flows to correctly capture the tail behavior and, thus, form more accurate models.
• We prove that the marginal tailedness of an autoregressive flow can be controlled via the tailedness of the marginals of its base distribution.