Learning to Fluctuate: Statistical Foundations for Causal Tabular Pretraining

arXiv:2609.26290 · stat.ML, cs.LG · Submitted 2026-09-22 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-24

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

Terminology

• Accuracy — 15× in this paper · explained in 1 episode
• Benchmark — 13× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Foundation Model — 6× in this paper · explained in 3 episodes
• Aggregation — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Exchangeability — 5× in this paper · explained in 2 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Foundation Models — 5× in this paper · explained in 7 episodes
• Causal Inference — 4× in this paper · explained in 4 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Provenance — 3× in this paper · explained in 4 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Data Provenance — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Frozen model — 2× in this paper · explained in 3 episodes
• In-Context Learning — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptivity — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Amortization — 1× in this paper · explained in 1 episode
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Calibration — 1× in this paper · explained in 7 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Fisher Information — 1× in this paper · explained in 3 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Objective Mismatch — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Optimal In-context Adaptivity and Distributional Robustness of Transformers
• Prior-Data Fitted Networks for Causal Inference: a Simulation Study with Real-World Scenarios

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