CRUMB speeds up PFN inference on large tabular datasets by clustering queries and selecting MMD-matched context subsets, outperforming prior selection methods on the 51-dataset TabArena benchmark across three architectures while handling covariate drift.
arXiv preprint arXiv:2402.11137 , eprint =
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A new constrained parametric bootstrap test for single-population ancestry in the supervised admixture model, proven to have asymptotic level alpha and consistency.
TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.
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CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
CRUMB speeds up PFN inference on large tabular datasets by clustering queries and selecting MMD-matched context subsets, outperforming prior selection methods on the 51-dataset TabArena benchmark across three architectures while handling covariate drift.
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Testing for Single-Population Ancestry in the Admixture Model
A new constrained parametric bootstrap test for single-population ancestry in the supervised admixture model, proven to have asymptotic level alpha and consistency.
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TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.