Review for NeurIPS paper: Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Summary and Contributions: 3rd EDIT: With the notebook now attached to this submission, I am content with the quality of the empirical evaluation. For double precision, the Cholesky decomposition fails less often whereas MINRES needs more iterations to converge. Since this aspect is neither explored nor even mentioned, I recommend to reject this paper. EDIT: In their rebuttal the authors just brushed over my concern that the number of iterations is limited to J 200. It appears this parameter is far more crucial than the authors are willing to admit and reproducing the experiments turned out to be difficult.