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.
Ensemble of averages: Im- proving model selection and boosting performance in domain generalization
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FGMix learns instance weights via gradient compatibilities to perform mixup with extrapolation toward flatter minima, outperforming prior DG methods on DomainBed.
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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.
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Learning Gradient-based Mixup with Extrapolation toward Flatter Minima for Domain Generalization
FGMix learns instance weights via gradient compatibilities to perform mixup with extrapolation toward flatter minima, outperforming prior DG methods on DomainBed.