Active Learning via Heteroskedastic Rational Kriging

TL;DR

This paper introduces a heteroskedastic Gaussian process model for active learning that efficiently targets regions of interest, improving surrogate model accuracy while reducing computational costs.

Contribution

The paper proposes a novel heteroskedastic Gaussian process model for active learning, enabling more targeted sampling and faster performance compared to existing non-stationary methods.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves similar or better results with less computational time

Effective on both simulated and real datasets

Abstract

Active learning methods for emulating complex computer models that rely on stationary Gaussian processes tend to produce design points that uniformly fill the entire experimental region, which can be wasteful for functions which vary only in small regions. In this article, we propose a new Gaussian process model that captures the heteroskedasticity of the function. Active learning using this new model can place design points in the more interesting regions of the response surface, and thus obtain surrogate models with better accuracy. The proposed active learning method is compared with the state-of-the-art methods using simulations and two real datasets. It is found to have comparable or better performance relative to other non-stationary Gaussian process-based methods, but faster by orders of magnitude.