Harnessing Multilevel Circulant Matrices for Generalizable Spectral Kernel Learning.
SpectraMancer, which learns kernels directly in the Fourier spectral domain induced by multilevel circulant matrices, thereby enabling generalizable kernel learning for complex data and improves spectrum-aware kernel selection and predictive performance across diverse benchmarks.