Sparse GP Approximations

Abstract

I present an introduction to pseudo-input methods for sparse GP approximations. I derive the variational lower bounds for SGPR and SVGP and give some intution for how they should be interpreted.

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.