CONSERVAttack creates adversarial perturbations in HEP ML models that respect uncertainty bounds but cause misclassifications, revealing gaps in current validation practices.
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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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Shapes are not enough: CONSERVAttack and its use for finding vulnerabilities and uncertainties in machine learning applications
CONSERVAttack creates adversarial perturbations in HEP ML models that respect uncertainty bounds but cause misclassifications, revealing gaps in current validation practices.
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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.