Abstract
I argue that the task a model will be used for should be an explicit part of the modelling process. As an example, I show how ideas from Bayesian Optimization and Probabilistic Numberics can be used to reinterpret Reinforcement Learning.
Publication
Workshop on Uncertainty in Composite Models
![Markus Kaiser](/author/markus-kaiser/avatar_hu9ac0b1177f8c5e5d524fd0452ad68992_424407_270x270_fill_q90_lanczos_center.jpg)
Research Scientist
Research Associate at the University of Cambridge and Research Scientist at Siemens AG. I am interested in scalable Bayesian machine learning and Gaussian processes.