Uncertainties Need a Purpose

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.

Markus Kaiser

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.