Context-Constrained Transfer Learning for Tabular Foundation Models via Data Distillation
Tabular Foundation Models (TFMs) have demonstrated strong empirical performance as black-box inference engines through in-context learning. However, their use in transfer learning is limited by two obstacles: strict context-size constraints and sensitivity to distribution shifts between source and target tasks. Directly pooling heterogeneous source data can therefore lead to negative transfer. To address these challenges, we propose Context-Constrained Transfer Learning via ANchoring and DIstillation (TL-ANDI), a posterior-aware distillation framework for TFMs. TL-ANDI constructs a compact source context by solving a budget-constrained optimal transport problem whose cost jointly measures...
| Authors | Yijun Lin, Sai Li |
|---|---|
| Primary category | stat.ML |
| Published | 6 July 2026 |
| Last updated at source | 6 July 2026 |
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