On Kernel Regression with Data-Dependent Kernels. (arXiv:2209.01691v2 [cs.LG] UPDATED)

The primary hyperparameter in kernel regression (KR) is the choice of kernel.
In most theoretical studies of KR, one assumes the kernel is fixed before
seeing the training data. Under this assumption, it is known that the optimal
kernel is equal to the prior covariance of the target function. In this note,
we consider KR in which the kernel may be updated after seeing the training
data. We point out that an analogous choice of kernel using the posterior of …

arxiv data kernel regression