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

arXiv:2607.04809 · stat.ML, cs.LG · Submitted 2026-07-06 · Read on arXiv

Yijun Lin, Sai Li

stat.ML, cs.LG

Submitted: 2026-07-06

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Distillation — 64× in this paper · explained in 2 episodes
• Transfer Learning — 34× in this paper · explained in 16 episodes
• Distribution Shift — 12× in this paper · explained in 12 episodes
• Foundation Model — 12× in this paper · explained in 3 episodes
• Calibration — 11× in this paper · explained in 9 episodes
• In-Context Learning — 11× in this paper · explained in 7 episodes
• Optimal Transport — 10× in this paper · explained in 5 episodes
• Robustness — 10× in this paper · explained in 6 episodes
• Foundation Models — 9× in this paper · explained in 8 episodes
• Mean Squared Error — 9× in this paper
• Distribution Shifts — 7× in this paper · explained in 2 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Benchmark — 4× in this paper · explained in 2 episodes
• Mean Prediction Error — 4× in this paper
• Tabular Foundation Models (TFMs) — 4× in this paper · explained in 2 episodes
• Ablation Study — 3× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Theoretical Guarantees — 3× in this paper · explained in 2 episodes
• Covariate Shift — 2× in this paper · explained in 3 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Performance Gap — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Variance Reduction — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• In-Context Learning (ICL) — 1× in this paper · explained in 11 episodes
• Minimax Optimality — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes
• Transferability — 1× in this paper · explained in 5 episodes
• Two-Stage Architecture — 1× in this paper · explained in 2 episodes

Sources

• TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
• On Learning Representations for Tabular Data Distillation
• TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
• A Closer Look at TabPFN v2: Understanding Its Strengths and Extending Its Capabilities
• LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence

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