Hierarchical Models for Insightful Machine Learning

Abstract

I argue why hierarchical Bayesian models are a good fit for real-world applications that rely on trustworthy and interpretable ML. By means of two industrial examples I show how such hierarchical models can be formulated from expert knowledge.

Publication
Accelerate Science Winter School 2021
Markus Kaiser
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.