M. D. Rijke

Prior-Data Fitted Networks as Tabular Foundation Models for Ranking in Low-Data Settings

This work demonstrates that PFNs, which are originally trained for classification, successfully outperform classification baselines on ranking data and introduces a novel sampling and inference scheme to obtain pairwise predictions from PFNs' native pointwise architecture, analogous to pairwise LTR.