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  1. One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent ad- vances in inducing point methods have sped up GP marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remaining computational bottlenecks. In this paper we address these shortcom- ings by using the Lanczos algorithm to rapidly ap- proximate the predictive covariance matrix. Our approach, which we refer to as LOVE (LanczOs Variance Estimates), substantially improves time and space complexity. In our experiments, LOVE computes covariances up to 2,000 times faster and draws samples 18,000 times faster than existing methods, all without sacrificing accuracy.