Reviews: Scaling Gaussian Process Regression with Derivatives

The paper investigates the application of the recent SKI and SKIP GP methodologies in the case where additional gradient information is available. They derive the necessary mathematical background, discuss numerical strategies to make the computations robust efficient and efficient and evaluate extensively on established synthetic and real benchmark problems. A weakness of the paper is the somewhat entangled discussion of dimensionality reduction strategies. D-SKI scales with 6 d, which is approximately 60 million for d 10 and roughly half a trillion for d 15. These numbers demonstrate that D-SKIP will not be optional for many problems typically considered with BO.