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Gaussian processes are Bayesian non-parametric models used in many areas. Inthis work, we propose a Non-stationary Heteroscedastic Gaussian process modelwhich can be learned with gradient-based techniques. We demonstrate theinterpretability of the proposed model by separating the overall uncertaintyinto aleatoric (irreducible) and epistemic (model) uncertainty. We illustratethe usability of derived epistemic uncertainty on active learning problems. Wedemonstrate the efficacy of our model with various ablations on multipledatasets.

Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning