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.
The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural net- works
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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.