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ARXIV:2606.03347 · GENERATIVE MODELS · SUBMITTED 03 JUN · 20:43 UTC · FRESHNESS FRESH
ARXIV:2606.03347GENERATIVE MODELSSUBMITTED 03 JUN · 20:43 UTCFRESHNESS FRESHJungkyu Kim · Taeyoung Park · Kibok Lee · arXiv
AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values.
Opportunity summary
Pain AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values.
Evidence 0 refs | 3 sources | 50% coverage
Blocker Evidence unverified
AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values. We propose AugMask, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data by separating conditioning…
Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We connect this training rule to a Rao--Blackwellized objective and show that marginalizing missing entries yields a variance-weighted sensitivity penalty, discouraging over-reliance on uncertain…
Generative Models moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
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AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values.
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10.48550/arXiv.2606.03347AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values.
Abstract
Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We propose AugMask, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data by separating conditioning from supervision. AugMask 1) constructs numeric inputs via conditional stochastic augmentation using lightweight auxiliary models, and 2) applies denoising supervision only to observed coordinates. In effect, augmented missing entries serve as uncertain conditioning context rather than training targets. We connect this training rule to a Rao--Blackwellized objective and show that marginalizing missing entries yields a variance-weighted sensitivity penalty, discouraging over-reliance on uncertain completions. Across diverse datasets and missingness regimes, AugMask enables standard diffusion-based tabular generators to outperform specialized missing-aware baselines.
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unverified0 refs; 3 sources; 50% coverage.
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Dimensions overall score 7.0
PROBLEM
AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values. We propose AugMask, a plug-and-play training framework that adapts missing-unaware backbones to incomplete data by separating conditioning from supervisio...
METHOD
Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully specified inputs, whereas real-world tabular data often contain missing values. We propose AugMask,...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We connect this training rule to a Rao--Blackwellized objective and show that marginalizing missing entries yields a variance-weighted sensitivity penalty, discouraging over-reliance on uncertain completi...
WHY NOW
Generative Models moved forward this cycle; last verified June 2026. Public score 7.0/10. Production flags indicate code availability.
{"file name": "input.pdf", "number of pages": 33, "author": "Jungkyu Kim; Taeyoung Park; Kibok Lee", "title": "AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking"
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AugMask is a plug-and-play framework that enables diffusion models to effectively generate tabular data with missing values.
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