  Gaussian processes are Bayesian non-parametric models used in many areas. In
this work, we propose a Non-stationary Heteroscedastic Gaussian process model
which can be learned with gradient-based techniques. We demonstrate the
interpretability of the proposed model by separating the overall uncertainty
into aleatoric (irreducible) and epistemic (model) uncertainty. We illustrate
the usability of derived epistemic uncertainty on active learning problems. We
demonstrate the efficacy of our model with various ablations on multiple
datasets.
