Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

ArXi:2605.26067v1 Announce Type: cross Conditionally positive definite (CPD) kernels are defined with respect to a function class $\mathcal{F}$. It is well known that such a kernel $K$ is associated with its native space (defined analogously to an RKHS), which in turn gives rise to a learning method -- called conditional kernel ridge regression (conditional KRR) due to its analogy with KRR -- where the estimated regression function is penalized by the square of its native space norm.