Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Summary
Proposes TAP, a tabular augmentation policy that couples diffusion inpainting with a learner-conditioned policy to improve downstream model performance under data scarcity, outperforming strong baselines on real-world datasets.
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Paper page - Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Source: https://huggingface.co/papers/2605.10315
Abstract
Tabular augmentation policy combines diffusion inpainting with a learner-conditioned policy to improve downstream model performance under data scarcity.
Generative tabular augmentationis appealing indata-scarce domains, yet the prevailing focus ondistributional fidelitydoes not reliably translate into better downstream models. We formalize afidelity-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 couplesdiffusion inpaintingwith a lightweight,learner-conditioned policyto steer generation toward high-utility regions and controls safe injection viaexplicit gatingandconservative 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%.
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