4 research outputs found
Gaussian Process Boosting
We introduce a novel way to combine boosting with Gaussian process and mixed
effects models. This allows for relaxing, first, the linearity assumption for
the mean function in Gaussian process and grouped random effects models in a
flexible non-parametric way and, second, the independence assumption made in
most boosting algorithms. The former is advantageous for predictive accuracy
and for avoiding model misspecifications. The latter is important for more
efficient learning of the mean function and for obtaining probabilistic
predictions. In addition, we present an extension that scales to large data
using a Vecchia approximation for the Gaussian process model relying on novel
results for covariance parameter inference. We obtain increased predictive
accuracy compared to existing approaches on several simulated and real-world
data sets