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

Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We study in these models a decomposition of predictive uncertainty into its epistemic and aleatoric components. We show how such a decomposition arises naturally in a Bayesian active learning scenario and develop a new objective for reliable reinforcement learning (RL) with an epistemic and aleatoric risk element. Our experiments illustrate the usefulness of the resulting decomposition in active learning and reliable RL.