  We present Blitzkriging, a new approach to fast inference for Gaussian
processes, applicable to regression, optimisation and classification.
State-of-the-art (stochastic) inference for Gaussian processes on very large
datasets scales cubically in the number of 'inducing inputs', variables
introduced to factorise the model. Blitzkriging shares state-of-the-art scaling
with data, but reduces the scaling in the number of inducing points to
approximately linear. Further, in contrast to other methods, Blitzkriging: does
not force the data to conform to any particular structure (including
grid-like); reduces reliance on error-prone optimisation of inducing point
locations; and is able to learn rich (covariance) structure from the data. We
demonstrate the benefits of our approach on real data in regression,
time-series prediction and signal-interpolation experiments.
