JM Hernández-Lobato

Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft et al.

We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class and illustrate the usefulness of our sensitivity analysis on real-world datasets. Our method increases the interpretability of complex black-box probabilistic models.