Active Tabular Augmentation via Policy-Guided Diffusion Inpainting

MCML Authors

Zheyu Zhang

→ Group Gjergji Kasneci
 Responsible Data Science

Shuo Yang

→ Group Gjergji Kasneci
 Responsible Data Science

Bardh Prenkaj

Dr.

→ Group Gjergji Kasneci
 Responsible Data Science

Gjergji Kasneci

Prof. Dr.

Core PI

Responsible Data Science

Abstract

Generative tabular augmentation is appealing in data-scarce domains, yet the prevailing focus on distributional fidelity does not reliably translate into better downstream models. We formalize a fidelity-utility gap: common generative objectives prioritize distributional plausibility, whereas augmentation succeeds only when injected samples reduce the current learner's held-out evaluation loss. This gap motivates learning not just how to generate, but what to generate and when to inject as training evolves. We propose TAP (Tabular Augmentation Policy), which couples diffusion inpainting with a lightweight, learner-conditioned policy to steer generation toward high-utility regions and controls safe injection via explicit gating and conservative windowed commitment. Under severe data scarcity, TAP consistently outperforms strong generative baselines on seven real-world datasets, improving classification accuracy by up to 15.6 percentage points and reducing regression RMSE by up to 32%.

inproceedings ZYP+26

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ICML 2026

43rd International Conference on Machine Learning. Seoul, South Korea, Jul 06-11, 2026. To be published. Preprint available.

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Authors

Z. Zhang • S. Yang • B. Prenkaj • G. Kasneci

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Links

URL GitHub

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Research Area

 A1 | Statistical Foundations & Explainability