5 citations · 5 across the 1 of their papers we have counts for
2 papers
cs.LG2019★ 5 cited
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
Markus Kaiser, Clemens Otte, Thomas Runkler +1
In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learni…
stat.ML2018
Data Association with Gaussian Processes
Markus Kaiser, Clemens Otte, Thomas Runkler +1
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significa…