  Gaussian process (GP) predictors are an important component of many Bayesian
approaches to machine learning. However, even a straightforward implementation
of Gaussian process regression (GPR) requires O(n^2) space and O(n^3) time for
a dataset of n examples. Several approximation methods have been proposed, but
there is a lack of understanding of the relative merits of the different
approximations, and in what situations they are most useful. We recommend
assessing the quality of the predictions obtained as a function of the compute
time taken, and comparing to standard baselines (e.g., Subset of Data and
FITC). We empirically investigate four different approximation algorithms on
four different prediction problems, and make our code available to encourage
future comparisons.
