Conformal Prediction and Conditional Coverage for Tabular Foundation Models

arXiv:2609.34887 · stat.ML, cs.LG · Submitted 2026-09-28 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-28

Updated: 2026-09-28

Code: https://github.com/priorlabs/tabpfn2https:

Terminology

• Calibration — 78× in this paper · explained in 7 episodes
• Conformal Prediction — 16× in this paper · explained in 5 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Foundation Model — 7× in this paper · explained in 3 episodes
• Foundation Models — 6× in this paper · explained in 7 episodes
• Clustering — 5× in this paper · explained in 2 episodes
• Exchangeability — 4× in this paper · explained in 2 episodes
• Generative AI — 4× in this paper · explained in 2 episodes
• In-Context Learning — 3× in this paper · explained in 4 episodes
• Normalizing Flow — 3× in this paper · explained in 3 episodes
• Normalizing Flows — 3× in this paper · explained in 4 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Conditionally-Uniformized Score Integration Method — 2× in this paper
• National Research Foundation of Korea — 2× in this paper
• Transformer — 2× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• On Optimal Data Splitting for Split Conformal Prediction
• TabPFN-3: Technical Report
• A Post-Processing Conformal Prediction Approach for Conditional Coverage via Pivotal Scores
• Bayes' Power for Explaining In-Context Learning Generalizations

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