Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
Theory of reproducing kernels.Transactions of the American mathematical society, 68(3):337–404, 1950
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xRFM merges kernel-based feature learning with tree structures for scalable, interpretable tabular modeling and reports top performance on 100 regression and competitive results on 200 classification datasets versus 31 baselines including GBDTs and TabPFNv2.
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Kernel Embeddings and the Separation of Measure Phenomenon
Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
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xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
xRFM merges kernel-based feature learning with tree structures for scalable, interpretable tabular modeling and reports top performance on 100 regression and competitive results on 200 classification datasets versus 31 baselines including GBDTs and TabPFNv2.