Self-explaining variational posterior distributions for Gaussian Process models

TL;DR

This paper introduces self-explaining variational posterior distributions for Gaussian Process models, enhancing transparency and allowing incorporation of prior knowledge about the target function and individual features.

Contribution

It proposes a novel self-explaining variational posterior for Gaussian Processes, improving interpretability and prior knowledge integration in Bayesian models.

Findings

Enhanced model transparency

Ability to incorporate feature-specific prior knowledge

Improved understanding of model reasoning

Abstract

Bayesian methods have become a popular way to incorporate prior knowledge and a notion of uncertainty into machine learning models. At the same time, the complexity of modern machine learning makes it challenging to comprehend a model's reasoning process, let alone express specific prior assumptions in a rigorous manner. While primarily interested in the former issue, recent developments intransparent machine learning could also broaden the range of prior information that we can provide to complex Bayesian models. Inspired by the idea of self-explaining models, we introduce a corresponding concept for variational GaussianProcesses. On the one hand, our contribution improves transparency for these types of models. More importantly though, our proposed self-explaining variational posterior distribution allows to incorporate both general prior knowledge about a target function as a whole and prior knowledge about the contribution of individual features.