Decomposition of Uncertainty for Active Learning and Reliable Reinforcement Learning in Stochastic Systems
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
Near-optimal Regret Bounds for Reinforcement Learning
Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
An information-theoretic approach to curiosity-driven reinforcement learning