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Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
We study how the objective used to train or fine-tune a tabular foundation model affects its distributional predictions. Models such as TabPFN and TabICL provide forecasts of the full conditional distribution, but regression benchmarks including TabArena and TALENT assess them mainly through RMSE an
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