Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
© 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved. Bayesian neural networks with latent variables are scalable and flexible probabilistic models: They account for uncertainty in the estimation of the network weights and, by making use of latent variables, can capture complex noise patterns in the : data. Using these models we show how to per- ∗ form and utilize a decomposition of uncertainty in • aleatoric and epistemic components for decision making purposes. This allows us to successfully identify informative points for active learning of functions with heteroscedastic and bimodal noise. ' t Using the decomposition we further define a novel risk-sensitive criterion for reinforcement learning to identify policies that balance expected cost, model-bias and noise aversion.