PIQL integrates train-time-only privileged information into tabular foundation models via new constructions and a reconstruction architecture to achieve faster convergence and better generalization.
TabPFN: A transformer that solves small tabular classification problems in a second
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.
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Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
PIQL integrates train-time-only privileged information into tabular foundation models via new constructions and a reconstruction architecture to achieve faster convergence and better generalization.
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Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
A tabular foundation model with LLM-as-Observer features predicts AI agent decisions in controlled games, outperforming baselines by 4 AUC points and 14% lower error at K=16 interactions.