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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3 Pith papers cite this work, alongside 14 external citations. Polarity classification is still indexing.
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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.
A survey that proposes a lifecycle-centric framework and the Financial AI Security and Robustness Taxonomy to organize 17 attack subtypes on AI pipelines in finance.
citing papers explorer
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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.
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When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech
A survey that proposes a lifecycle-centric framework and the Financial AI Security and Robustness Taxonomy to organize 17 attack subtypes on AI pipelines in finance.