Related papers: Copula-Based Normalizing Flows

Copula-Based Normalizing Flows

URL: http://arxiv.org/abs/2107.07352v1
Date: Thu, 15 Jul 2021 14:22:28 GMT
Title: Copula-Based Normalizing Flows
Authors: Mike Laszkiewicz, Johannes Lederer, Asja Fischer
Abstract summary: We generalize the base distribution to a more elaborate copula distribution to capture the properties of the target distribution more accurately. Our results suggest that the improvements are related to an increased local Lipschitz-stability of the learned flow.
Score: 8.894004971395546
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Normalizing flows, which learn a distribution by transforming the data to samples from a Gaussian base distribution, have proven powerful density approximations. But their expressive power is limited by this choice of the base distribution. We, therefore, propose to generalize the base distribution to a more elaborate copula distribution to capture the properties of the target distribution more accurately. In a first empirical analysis, we demonstrate that this replacement can dramatically improve the vanilla normalizing flows in terms of flexibility, stability, and effectivity for heavy-tailed data. Our results suggest that the improvements are related to an increased local Lipschitz-stability of the learned flow.

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