Latent Map Gaussian Processes for Mixed Variable Metamodeling

TL;DR

This paper introduces latent map Gaussian processes (LMGPs), a novel method that extends Gaussian processes to handle mixed qualitative and quantitative variables by learning a low-dimensional latent manifold.

Contribution

The paper proposes LMGPs, enabling GPs to model mixed data types through a learned latent space, improving accuracy and interpretability over existing methods.

Findings

LMGPs outperform state-of-the-art methods in accuracy.

LMGPs handle variable-length inputs effectively.

LMGPs provide insights into qualitative variable effects.

Abstract

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. In this paper, we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.