Adaptive Importance Sampling Via Auto-Regressive Generative Models and Gaussian Processes

• Published in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Vol. 2021; pp. 5584 - 5588
• Main Authors: Wang, Hechuan, Bugallo, Monica F., Djuric, Petar M.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.01.2021
• Subjects: Adaptation models; adaptive importance sampling; Conferences; Estimation; Gaussian Process; Gaussian processes; generative model; Monte Carlo methods; population Monte Carlo; Signal processing; Training
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP39728.2021.9414734

The quality of importance distribution is vital to adaptive importance sampling, especially in high dimensional sampling spaces where the target distributions are sparse and hard to approximate. This requires that the proposal distributions are expressive and easily adaptable. Because of the need for weight calculation, point evaluation of the proposal distributions is also needed. The Gaussian process has been proven to be a highly expressive non-parametric model for conditional density estimation whose training process is also straightforward. In this paper, we introduce a class of adaptive importance sampling methods where the proposal distribution is constructed in a way that Gaussian processes are combined autoregressively. By numerical experiments of sampling from a high dimensional target distribution, we demonstrate that the method is accurate and efficient compared to existing methods.