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

• URL: http://arxiv.org/abs/2210.10964v1
• Date: Thu, 20 Oct 2022 02:18:19 GMT
• Title: Uncertainty Disentanglement with Non-stationary Heteroscedastic Gaussian Processes for Active Learning
• Authors: Zeel B Patel, Nipun Batra, Kevin Murphy
• Abstract summary: 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.
• Score: 10.757942829334057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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