Gaussian processes regression models are an appealing machine learning method as they learn expressive nonlinear from exemplar data with minimal parameter tuning and estimate both the mean covariance of unseen points. However, cubic computational complexity growth number samples has been a long standing challenge. Training requires inversion $N \times N$N×N kernel at every iteration, whereas ne...