Explainable Learning with Gaussian Processes
Kurt Butler Guanchao Feng Petar M. Djuric
The field of explainable artificial intelligence (XAI) attempts to develop
methods that provide insight into how complicated machine learning methods make
predictions. Many methods of explanation have focused on the concept of feature
attribution, a decomposition of the model's prediction into individual
contributions corresponding to each input feature. In this work, we explore the
problem of feature attribution in the context of Gaussian process regression
(GPR). We take a principled approach to defining attributions under model
uncertainty, extending the existing literature. We show that although GPR is a
highly flexible and non-parametric approach, we can derive interpretable,
closed-form expressions for the feature attributions. When using integrated
gradients as an attribution method, we show that the attributions of a GPR
model also follow a Gaussian process distribution, which quantifies the
uncertainty in attribution arising from uncertainty in the model. We
demonstrate, both through theory and experimentation, the versatility and
robustness of this approach. We also show that, when applicable, the exact
expressions for GPR attributions are both more accurate and less
computationally expensive than the approximations currently used in practice.
The source code for this project is freely available under MIT license at
https://github.com/KurtButler/2024_attributions_paper.