Researchers explored the decision-making process of Gaussian process (GP) models, focusing on loss landscapes and ...

This article explains how to create and use Gaussian process regression (GPR) models. Compared to other regression techniques, GPR is especially useful when there is limited training data. There are ...

We argue that counterfactual reasoning is ideal for interpreting model behavior, and that Gaussian processes (GP) can provide approximate counterfactual reasoning while also incorporating uncertainty ...

We model the joint posterior of the derivatives as a Gaussian process over function space, imposing the spatial covariancestructure on the risk factors. Monte Carlo simulation is then used to simulate ...

For example, a Gaussian Process is a flexible Bayesian model that enables feature engineering and incorporating domain expertise, and the predictions are intuitive to trace back to underlying ...