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We present Blitzkriging, a new approach to fast inference for Gaussianprocesses, applicable to regression, optimisation and classification.State-of-the-art (stochastic) inference for Gaussian processes on very largedatasets scales cubically in the number of 'inducing inputs', variablesintroduced to factorise the model. Blitzkriging shares state-of-the-art scalingwith data, but reduces the scaling in the number of inducing points toapproximately linear. Further, in contrast to other methods, Blitzkriging: doesnot force the data to conform to any particular structure (includinggrid-like); reduces reliance on error-prone optimisation of inducing pointlocations; and is able to learn rich (covariance) structure from the data. Wedemonstrate the benefits of our approach on real data in regression,time-series prediction and signal-interpolation experiments.

Blitzkriging: Kronecker-structured Stochastic Gaussian Processes