  Gaussian processes scale prohibitively with the size of the dataset. In
response, many approximation methods have been developed, which inevitably
introduce approximation error. This additional source of uncertainty, due to
limited computation, is entirely ignored when using the approximate posterior.
Therefore in practice, GP models are often as much about the approximation
method as they are about the data. Here, we develop a new class of methods that
provides consistent estimation of the combined uncertainty arising from both
the finite number of data observed and the finite amount of computation
expended. The most common GP approximations map to an instance in this class,
such as methods based on the Cholesky factorization, conjugate gradients, and
inducing points. For any method in this class, we prove (i) convergence of its
posterior mean in the associated RKHS, (ii) decomposability of its combined
posterior covariance into mathematical and computational covariances, and (iii)
that the combined variance is a tight worst-case bound for the squared error
between the method's posterior mean and the latent function. Finally, we
empirically demonstrate the consequences of ignoring computational uncertainty
and show how implicitly modeling it improves generalization performance on
benchmark datasets.

Machine Learning;Numerical Analysis;Numerical Analysis;Machine Learning