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This research addresses critical reliability gaps in tabular foundation models by introducing C-USIM, a method that ensures valid prediction intervals without requiring additional training or inference overhead. By leveraging split conformal prediction techniques, the study demonstrates how to mitigate undercoverage and overcoverage issues inherent in multimodal regression tasks using existing model outputs. The findings offer practical guidance for deploying trustworthy AI systems where uncertainty quantification is essential for decision-making.
Conformal Prediction and Conditional Coverage for Tabular Foundation Models
arXiv:2609.34887v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcoverage even when point predictions are accurate. We introduce C-USIM (Conditionally-Uniformized Score Integration Method), a lightweight application of highest predictive density split conformal prediction that accommodates multimodal predictions. Given calibration and test outputs, it requires no additional training or model inference. It provides finite-sample marginal validity under our assumptions. We bound conditional-marginal coverage gaps using distribution-estimation error and score discreteness, and examine coverage heterogeneity through percentile rank-score plots. Experiments with TabPFN and TabICL show improved marginal coverage accuracy and lower average conditional and group coverage errors. Under a fixed data budget, allocating more observations to calibration can reduce marginal coverage error despite less accurate point predictions.
Abstract:Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcoverage even when point predictions are accurate. We introduce C-USIM (Conditionally-Uniformized Score Integration Method), a lightweight application of highest predictive density split conformal prediction that accommodates multimodal predictions. Given calibration and test outputs, it requires no additional training or model inference. It provides finite-sample marginal validity under our assumptions. We bound conditional-marginal coverage gaps using distribution-estimation error and score discreteness, and examine coverage heterogeneity through percentile rank-score plots. Experiments with TabPFN and TabICL show improved marginal coverage accuracy and lower average conditional and group coverage errors. Under a fixed data budget, allocating more observations to calibration can reduce marginal coverage error despite less accurate point predictions.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2609.34887 [stat.ML]
(or arXiv:2609.34887v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2609.34887 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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