113 citations · 208 across the 11 of their papers we have counts for
4 papers · 1 filter
Sensitivity Analysis for Predictive Uncertainty in Bayesian Neural Networks
Stefan Depeweg, José Miguel Hernández-Lobato, Steffen Udluft +1
We derive a novel sensitivity analysis of input variables for predictive epistemic and aleatoric uncertainty. We use Bayesian neural networks with latent variables as a model class…
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
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…
A Benchmark Environment Motivated by Industrial Control Problems
Daniel Hein, Stefan Depeweg, Michel Tokic +4
In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are…
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables
Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez +1
Bayesian neural networks (BNNs) with latent variables are probabilistic models which can automatically identify complex stochastic patterns in the data. We describe and study in th…