Self-explaining variational posterior distributions for Gaussian Process models
This work addresses the problem of model transparency and prior incorporation in Bayesian machine learning for researchers and practitioners, but it appears incremental as it builds on existing self-explaining models and variational methods.
The paper tackles the challenge of making Gaussian Process models more transparent and incorporating prior knowledge by introducing self-explaining variational posterior distributions, which improve model interpretability and allow integration of both general and feature-specific priors.
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.