  We present a method to approximate Gaussian process regression models for
large datasets by considering only a subset of the data. Our approach is novel
in that the size of the subset is selected on the fly during exact inference
with little computational overhead. From an empirical observation that the
log-marginal likelihood often exhibits a linear trend once a sufficient subset
of a dataset has been observed, we conclude that many large datasets contain
redundant information that only slightly affects the posterior. Based on this,
we provide probabilistic bounds on the full model evidence that can identify
such subsets. Remarkably, these bounds are largely composed of terms that
appear in intermediate steps of the standard Cholesky decomposition, allowing
us to modify the algorithm to adaptively stop the decomposition once enough
data have been observed.

Machine Learning