Variational Determinant Estimation with Spherical Normalizing Flows
Machine Learning
2021-01-11 v3 Machine Learning
Abstract
This paper introduces the Variational Determinant Estimator (VDE), a variational extension of the recently proposed determinant estimator discovered by arXiv:2005.06553v2. Our estimator significantly reduces the variance even for low sample sizes by combining (importance-weighted) variational inference and a family of normalizing flows which allow density estimation on hyperspheres. In the ideal case of a tight variational bound, the VDE becomes a zero variance estimator, and a single sample is sufficient for an exact (log) determinant estimate.
Cite
@article{arxiv.2012.13311,
title = {Variational Determinant Estimation with Spherical Normalizing Flows},
author = {Simon Passenheim and Emiel Hoogeboom},
journal= {arXiv preprint arXiv:2012.13311},
year = {2021}
}
Comments
Accepted at 3rd Symposium on Advances in Approximate Bayesian Inference (AABI) 2021