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 and $R^2$. Such assessments reward accurate conditional means while leaving the remaining distributional information unassessed. We therefore examine proper scoring rules as objectives for training and fine-tuning and as criteria for evaluating and ranking these models. Every strictly proper rule favors the true distribution at the population level, however, this shared optimum does not determine...
| Authors | Jonas Landsgesell, Pascal Knoll, Tizian Wenzel |
|---|---|
| Primary category | cs.LG |
| Published | 9 March 2026 |
| Last updated at source | 15 October 2026 |
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