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Bayesian neural networks with latent variables are scalable and flexibleprobabilistic models: They account for uncertainty in the estimation of thenetwork weights and, by making use of latent variables, can capture complexnoise patterns in the data. We show how to extract and decompose uncertaintyinto epistemic and aleatoric components for decision-making purposes. Thisallows us to successfully identify informative points for active learning offunctions with heteroscedastic and bimodal noise. Using the decomposition wefurther define a novel risk-sensitive criterion for reinforcement learning toidentify policies that balance expected cost, model-bias and noise aversion.

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning