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MissDiff: Training Diffusion Models on Tabular Data with Missing Values
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The diffusion model has shown remarkable performance in modeling data distributions and synthesizing data. However, the vanilla diffusion model requires complete or fully observed data for training. Incomplete data is a common issue in various real-world applications, including healthcare and finance, particularly when dealing with tabular datasets. This work presents a unified and principled diffusion-based framework for learning from data with missing values under various missing mechanisms. We first observe that the widely adopted "impute-then-generate" pipeline may lead to a biased learning objective. Then we propose to mask the regression loss of Denoising Score Matching in the training phase. We prove the proposed method is consistent in learning the score of data distributions, and the proposed training objective serves as an upper bound for the negative likelihood in certain cases. The proposed framework is evaluated on multiple tabular datasets using realistic and efficacious metrics and is demonstrated to outperform state-of-the-art diffusion model on tabular data with "impute-then-generate" pipeline by a large margin.
Forward citations
Cited by 8 Pith papers
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Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness
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CoreFlow is a low-rank matrix generative model that trains normalizing flows on shared subspaces to improve efficiency and quality for high-dimensional limited-sample data, including incomplete matrices.
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Gradients of any differentiable tabular constraint lie in the tangent space of the data manifold, enabling a single trained diffusion model to satisfy unseen conditions via inference-time guidance.
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Flow Matching with Missing Data
Resampling missing coordinates and averaging the flow-matching loss reproduces the complete-data objective exactly under MCAR with oracle completions; one completion per example is optimal for a fixed budget.
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AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
AugMask is a plug-and-play training framework that lets diffusion models on incomplete tabular data use stochastic augmentation for conditioning and observed-only supervision, outperforming missing-aware baselines via...
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Latent Diffusion for Missing Data
A VAE-based latent diffusion model trained on incomplete data maintains sample quality and imputation performance up to 50% missingness while pixel-space diffusion degrades.
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Incomplete Data, Complete Dynamics: A Diffusion Approach
A conditional diffusion model trained on partitioned incomplete samples for physical dynamics achieves asymptotic convergence to the true generative process under mild conditions and outperforms baselines in imputation.
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Evaluates 42 variants of foundation models across three formalized paradigms for missing modality reconstruction, identifies shortfalls in semantic extraction and validation, and introduces an agentic framework that r...
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