arXiv AI June 3, 2026 By Jungkyu Kim, Taeyoung Park, Kibok Lee

AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking

arXiv:2606. 03347v1 Announce Type: cross 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.

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diffusion

arXiv AI

4d ago

ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation

arXiv:2609.37632v1 Announce Type: cross Abstract: Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent perform...

By Fariza Rashid, Duc Van Le, Rahat Masood, Gustavo Batista, Aruna Seneviratne, Suranga Seneviratne

diffusion benchmarks

Hugging Face Trending Papers

Jun 3

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values. In many real-world datasets, however, missingness may arise from two distinct sources: some entries are meaningfully missing (intrinsically absent and semantically valid), while others are missing due to the observation process and should be imputed.

diffusion

arXiv Machine Learning

Jun 4

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

arXiv:2606. 05073v1 Announce Type: new Abstract: Missing value imputation is a fundamental task in machine learning, with most existing methods assuming that all missing entries correspond to unobserved regular values.

By Lixing Zhang, Yidong Ouyang, Weifu Li, Shixiang Zhu, Guang Cheng, Liyan Xie

diffusion

arXiv Machine Learning

Jun 5

Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion

arXiv:2602. 07875v3 Announce Type: replace Abstract: Generating tabular data under conditions is critical to applications requiring precise control over the generative process.

By Aditya Shankar, Yuandou Wang, Rihan Hai, Lydia Y. Chen

diffusion

arXiv Machine Learning

Aug 3

Flow Matching with Missing Data

arXiv:2607. 28698v1 Announce Type: new Abstract: Flow matching assumes fully observed training data, which many real-world applications rarely provide.

By Fairoz Nower Khan, Nabuat Zaman Nahim, Peizhong Ju

diffusion safety

arXiv Machine Learning

2d ago

CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data

arXiv:2609.39124v1 Announce Type: new Abstract: Generative models for tabular data are typically trained separately for each dataset, limiting knowledge transfer and requiring the storage of many spe...

By Mohamed Amine Ketata, Maximilian Schambach, Stephan G\"unnemann

llms diffusion

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Related stories

ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

Learning What Not to Impute: An Uncertainty-Aware Diffusion Framework for Meaningful Missingness

Harpoon: Generalised Manifold Guidance for Conditional Tabular Diffusion

Flow Matching with Missing Data

CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data