39d929972619274cc9066307f707d002-AuthorFeedback.pdf

We thank all the reviewers for their supportive and insightful comments. While kernel learning has now been1 broadly identified as important for good performance, the vast majority of approaches, while highly useful, focus2 on parametric methods that do not represent uncertainty over the values of the kernel, can be difficult to train, and3 difficult to specify inductive biases. In the camera ready, we will fix the typos and add in-text ref-33 erences to the figures we missed. Non-axis aligned methods are also possible35 with other generalizations of FFT (possibly [3]). Inthecameraready,wewillupdatethe40 figure to be on the count instead.9: