Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
📰 ArXiv cs.AI
Learn to augment tabular data using policy-guided diffusion inpainting to improve downstream model performance, addressing the fidelity-utility gap
Action Steps
- Formalize the fidelity-utility gap in generative tabular augmentation
- Implement policy-guided diffusion inpainting to generate augmented tabular data
- Evaluate the effectiveness of the augmented data in reducing held-out evaluation loss
- Apply the technique to data-scarce domains to improve downstream model performance
- Compare the results with traditional generative objectives
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this technique to improve the performance of their models in data-scarce domains, by generating high-quality augmented data
Key Insight
💡 The fidelity-utility gap in generative tabular augmentation can be addressed by learning to generate data that reduces held-out evaluation loss, rather than just prioritizing distributional plausibility
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🚀 Improve downstream model performance with policy-guided diffusion inpainting for tabular data augmentation! 💡
Key Takeaways
Learn to augment tabular data using policy-guided diffusion inpainting to improve downstream model performance, addressing the fidelity-utility gap
Full Article
Title: Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
Abstract:
arXiv:2605.10315v1 Announce Type: cross 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
Abstract:
arXiv:2605.10315v1 Announce Type: cross 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
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