Evaluation of Active Feature Acquisition Policies with Tabular Foundation Models

arXiv:2610.07406 · cs.LG · Submitted 2026-10-05 · Read on arXiv

cs.LG

Submitted: 2026-10-05

Updated: 2026-10-05

Code: https://github.com/reAIM-Lab/BayesianAFA

Terminology

• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes

Sources

• Non-Myopic Active Feature Acquisition via Pathwise Policy Gradients
• Variational Information Pursuit for Interpretable Predictions
• Uncertainty Decomposition for Bayes-Filtered Transformers via Bayesian Predictive Inference
• Estimating Conditional Mutual Information for Dynamic Feature Selection
• TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
• Opportunistic Learning: Budgeted Cost-Sensitive Learning from Data Streams
• Learning-To-Measure: In-Context Active Feature Acquisition
• EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
• Frequentist Consistency of Prior-Data Fitted Networks for Causal Inference
• Transformers Can Do Bayesian Inference
• Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
• TabMGP: Martingale Posterior with TabPFN
• TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
• Evaluation of Active Feature Acquisition Methods for Static Feature Settings

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