882 citations · 2.6k across the 89 of their papers we have counts for
3 papers · 2 filters
Prediction performance after learning in Gaussian process regression
Johan Wågberg, Dave Zachariah, Thomas B. Schön +1
This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical…
A sequential Monte Carlo approach to Thompson sampling for Bayesian optimization
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
Bayesian optimization through Gaussian process regression is an effective method of optimizing an unknown function for which every measurement is expensive. It approximates the obj…
System Identification through Online Sparse Gaussian Process Regression with Input Noise
Hildo Bijl, Thomas B. Schön, Jan-Willem van Wingerden +1
There has been a growing interest in using non-parametric regression methods like Gaussian Process (GP) regression for system identification. GP regression does traditionally have…