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.
Linear recursive feature machines provably recover low-rank matrices.Proceedings of the National Academy of Sciences, 122 (13):e2411325122
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K-Inverse-RFM applies a label transformation to Recursive Feature Machines to close the performance gap with neural networks on data-corrupted mathematical tasks.
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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.
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K-Inverse-RFM: A Modified RFM that Bridges the Gap to Neural Networks for Data-Corrupted Mathematical Tasks
K-Inverse-RFM applies a label transformation to Recursive Feature Machines to close the performance gap with neural networks on data-corrupted mathematical tasks.