Yi-Jun Lin

Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation

This work proposes Context-Constrained Transfer Learning via ANchoring and DIstillation (TL-ANDI), a posterior-aware distillation framework for TFMs that constructs a compact source context by solving a budget-constrained optimal transport problem whose cost jointly measures target covariate coverage and posterior compatibility.