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arXiv Machine Learning By Minho Jeong, Dooho Lee, Jinmo Lee, Jaemin Yoo

Distillation of Tabular Foundation Models into Efficient Predictors

Read the original on arXiv Machine Learning →

The paper presents a method for distilling tabular foundation models (TFMs) into lightweight, dataset‑specific students. By using the full labeled training set as teacher context and training students on both observed and synthetic queries, the authors achieve significant performance gains over traditional supervised models on TabArena and TALENT benchmarks. The distilled students also provide substantial inference speedups, reducing the cost of repeated inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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