Learning with Missing Data

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Order-Agnostic Autoregressive Modelling with Missing Data
May 7, 2026Ignacio Peis, Pablo M. Olmos, Jes FrellsenIncomplete Data Imputation Learning with Missing Data
Technical University of Denmark & Pioneer Centre for AI · Universidad Carlos III de Madrid
Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling
May 3, 2026Qiao LiuIncomplete Data Imputation Uncertainty Quantification
Department of Biostatistics Yale University
End-to-End Learning for Partially-Observed Time Series with PyPOTS
Apr 27, 2026Wenjie Du, Yiyuan Yang, Tianxiang Zhan +1Irregular Time-Series Modeling Time Series Imputation
PyPOTS Research · PyPOTS Research & Univ. of Oxford · Squirrel Ai Learning
Conditional Imputation for Within-Modality Missingness in Multi-Modal Federated Learning
Apr 25, 2026Wugeng Zheng, Ziwen Kan, Katie Wang +2Multimodal Federated Learning Time Series Imputation
University of Central Florida · Independent Researcher
Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data
Feb 1, 2026Yinsong Wang, Shahin ShahrampourNeural Network Generalization Learning with Missing Data
University of Nevada Reno, Reno, NV 89557 USA · Department of Mechanical and Industrial Engineering at Northeastern University, Boston, MA 02115 USA
Linear Regression under Missing or Corrupted Coordinates
Sep 23, 2025Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2Robust Regression Learning with Missing Data
University of Wisconsin-Madison · University of California, San Diego · University of California, Davis
No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels
Aug 15, 2025Yiyang Shen, Weiran WangLearning with Missing Data Multi-View Learning
Department of Computer Science, University of Iowa, Iowa City, Iowa, USA
Deep learning with missing data
Apr 21, 2025Tianyi Ma, Tengyao Wang, Richard J. SamworthNeural Network Generalization Learning with Missing Data
Statistical Laboratory, University of Cambridge · Department of Statistics, London School of Economics

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Order-Agnostic Autoregressive Modelling with Missing Data

Ignacio Peis, Pablo M. Olmos, Jes Frellsen

Technical University of Denmark & Pioneer Centre for AI · Universidad Carlos III de Madrid

Incomplete Data Imputation Learning with Missing Data Active Feature Acquisition Autoregressive Modeling

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Order-Agnostic autoregressive models have demonstrated strong performance in deep generative modeling, yet their use in settings with incomplete data remains largely unexplored. In this work, we reinterpret them through the lens of missing data. First, we show that their standard training procedure on fully observed data implicitly performs imputation under a missing completely at random mechanism, resulting in robust out-of-sample imputation performance in settings with high missingness. Second, we introduce the first principled framework for training them directly on incomplete datasets under general missingness mechanisms. Third, we leverage their amortized conditional density estimation to perform active information acquisition, i.e., sequentially selecting the most informative missing variables for downstream prediction or inference. Across a suite of real-world benchmarks, our Missingness-Aware Order-Agnostic Autoregressive Model (MO-ARM) consistently outperforms established imputation baselines.

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