Characteristic Circuits

Zhongjie Yu, Martin Trapp, Kristian Kersting

Keywords: Characteristic Circuit, Characteristic Function, Probabilistic Circuit, Heterogeneous Data, Density Estimation

TL;DR: Characteristic circuit is a deep probabilistic model over characteristic functions that enables a unified view for discrete and continuous random variables and allows to learn distributions that do not have closed form expressions for their density.

Abstract:

In many real-world scenarios it is crucial to be able to reliably and efficiently reason under uncertainty while capturing complex relationships in data.
Probabilistic circuits (PCs), a prominent family of tractable probabilistic models, offer a remedy to this challenge by composing simple, tractable distributions into a high-dimensional probability distribution.
However, learning PCs on heterogeneous data is challenging and densities of some parametric distributions are not available in closed form, limiting their potential use.
We introduce characteristic circuits (CCs), a family of tractable probabilistic models providing a unified formalization of distributions over heterogeneous data in the spectral domain.
The one-to-one relationship between characteristic functions and probability measures enables us to learn high-dimensional distributions on heterogeneous data domains and facilitates efficient probabilistic inference even when no closed-form density function is available.
We show that the structure and parameters of CCs can be learned efficiently from the data and find that CCs outperform state-of-the-art density estimators for heterogeneous data domains on common benchmark data sets.

Submission Number: 899