Learning with Missing Data

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Overview

The field studies how to use incomplete observations without introducing misleading estimates or discarding useful examples, especially when the chance of missingness depends on the data itself. Approaches range from likelihood-based methods, EM, and multiple imputation to Bayesian and diffusion generative models, masked transformers, and predictors that encode missingness patterns directly. Work spans tabular data, irregular time series, clinical records, graphs, and multimodal settings, with theory addressing identifiability, sample complexity, and uncertainty under MCAR, MAR, and MNAR mechanisms. A central tension is that missingness can be both a useful signal and a source of bias: methods must match the missingness process and downstream task, while evaluations increasingly test realistic patterns and train–test shifts rather than reconstruction error alone.

Latest papers

CardsList

120 papers22 papers in the last four weeks, up 22% on the four weeks before. 0.1% of all new papers.

  1. Cost-optimal Sequential Testing via Doubly Robust Q-learning

    Apr 13, 2026Doudou Zhou, Yiran Zhang, Dian Jin +3Q-LearningCost-Sensitive Learning

    Department of Statistics and Data Science, National University of Singapore · Public Health Sciences Division, Fred Hutch · Department of Biomedical Data Science, Stanford University School of Medicine +1

  2. Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows

    Apr 6, 2026Gitte Kremling, Jeffrey Näf, Johannes LedererLearning with Missing DataIncomplete Data Imputation

    Department of Mathematics, University of Hamburg · Research Institute for Statistics and Information Science, University of Geneva

  3. Uncertainty-Aware Foundation Models for Clinical Data

    Apr 5, 2026Qian Zhou, Yuanyun Zhang, Shi LiEpistemic UncertaintyMultimodal Foundation Models

    University of the Chinese Academy of Sciences · Columbia University

  4. Incomplete Multi-View Multi-Label Classification via Shared Codebook and Fused-Teacher Self-Distillation

    Apr 5, 2026Xu Yan, Jun Yin, Shiliang Sun +1Multi-Label ClassificationMulti-View Learning

    College of Information Engineering, Shanghai Maritime University · School of Automation and Intelligent Sensing, Shanghai Jiao Tong University

  5. DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks

    Mar 31, 2026Yan Lin, Jilin Hu, Shengnan Guo +3Learning with Missing DataConditional Distribution Estimation

    Aalborg University · East China Normal University · Beijing Jiaotong University

  6. Amortized Variational Inference for Logistic Regression with Missing Covariates

    Mar 22, 2026M. Cherifi, Aude Sportisse, Xujia Zhu +2Learning with Missing DataLogistic Regression

    Lab. Traitement du signal, Ecole Militaire Polytechnique, BP 17 Bordj El Bahri, Algeria · Lab. d’Informatique de Grenoble, CNRS, France · University of Paris Saclay, CNRS, CentraleSupelec, Laboratoire des signaux et systèmes, 91190, Gif-sur-Yvette, France

  7. High-dimensional estimation with missing data: Statistical and computational limits

    Mar 17, 2026Kabir Aladin Verchand, Ankit Pensia, Saminul Haque +1Learning with Missing DataCovariance Estimation

    Department of Data Sciences and Operations, University of Southern California · Department of Statistics and Data Science, Carnegie Mellon University · Department of Computer Science, Stanford University

  8. Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach

    Mar 16, 2026Parastoo Pashmchi, Jérôme Benoit, Motonobu KanagawaSolar Power ForecastingUncertainty Propagation

    SAP Labs France, Mougins, France · EURECOM, Biot, France

  9. Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC

    Mar 16, 2026Alice Natalina Caragliano, Giulia Farina, Fatih Aksu +10Clinical Outcome PredictionClinical Prediction

    Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy · Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy · Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy +1

  10. Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data

    Mar 12, 2026Keita Kayano, Takayuki Nishio, Daiki Yoda +2Wi-Fi SensingLearning with Missing Data

    School of Engineering, Institute of Science Tokyo, 2-12-1 Ookayama, Tokyo, 152-8550, Japan · Wireless Systems R&D Department, Infrastructure Systems R&D Center, Corporate Laboratory, Toshiba Corporation, 1, Komukai Toshiba-cho, Saiwai-ku, Kawasaki 212-8582, JAPAN

  11. Spatially Robust Inference with Predicted and Missing at Random Labels

    Mar 11, 2026Stephen Salerno, Zhenke Wu, Tyler McCormickConfidence Region EstimationLearning with Missing Data

    Fred Hutch Cancer Center · University of Michigan · University of Washington

  12. Uniform Inductive Spatio-Temporal Kriging

    Mar 5, 2026Lewei Xie, Haoyu Zhang, Yulong Chen +3Spatiotemporal ForecastingLearning with Missing Data

    City University of Hong Kong (Dongguan) · City University of Hong Kong

  13. Mask-aware inference with State-Space Models

    Mar 4, 2026Ignasi Mas, Ramon Morros, Javier-Ruiz Hidalgo +1Visual SSMsState Space Models

  14. MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction

    Mar 2, 2026Guanchen Wu, Zhe Huang, Yuzhang Xie +6Alzheimer's Disease ClassificationLearning with Missing Data

    Emory University, Atlanta, USA · Cornell University, New York, USA

  15. Active Value Querying to Minimize Additive Error in Subadditive Set Function Learning

    Feb 26, 2026Martin Černý, David Sychrovský, Filip Úradník +1Learning with Missing DataActive Learning

    Charles University, Prague, Czechia · Columbia University, New York, USA

  16. Robust Human Trajectory Prediction via Self-Supervised Skeleton Representation Learning

    Feb 26, 2026Taishu Arashima, Hiroshi Kera, Kazuhiko KawamotoHuman Trajectory PredictionTrajectory Prediction

    Chiba University

  17. T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation

    Feb 24, 2026Dongik Park, Hyunwoo Ryu, Suahn Bae +2Time Series ImputationLearning with Missing Data

    Graduate School of Data Science, Seoul National University

  18. Amortized Bayesian inference for actigraph time sheet data from mobile devices

    Feb 24, 2026Daniel Zhou, Sudipto BanerjeeAmortized InferenceWearable Health Monitoring

    UCLA DEPARTMENT OF BIOSTATISTICS, 650 CHARLES E. YOUNG DRIVE, LOS ANGELES, 90095-1772.

  19. Imputation of Unknown Missingness in Sparse Electronic Health Records

    Feb 24, 2026Jun Han, Josue Nassar, Sanjit Singh Batra +3Incomplete Data ImputationLearning with Missing Data

    Optum AI

  20. A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations

    Feb 23, 2026Dingyi Nie, Yixing Wu, C. -C. Jay KuoIrregular Time-Series ModelingMultivariate Time Series Classification

    University of Southern California

  21. Exploiting Completeness Perception with Diffusion Transformer for Unified 3D MRI Synthesis

    Feb 20, 2026Junkai Liu, Nay Aung, Theodoros N. Arvanitis +3Medical Image Synthesis3D Medical Imaging

    School of Engineering, University of Birmingham, UK · William Harvey Research Institute, Queen Mary University London, UK · Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, UK +1

  22. A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data

    Feb 19, 2026Justyna Andrys-Olek, Paulina Tworek, Luca Gherardini +4Interpretable MLLearning with Missing Data

    Computational Intelligence, Sano Centre for Computational Personalised Medicine, Krakow, Poland · Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom

  23. What is the Value of Censored Data? An Exact Analysis for the Data-driven Newsvendor

    Feb 18, 2026Rachitesh Kumar, Omar MouchtakiInventory ControlMinimax Regret

    Carnegie Mellon University · New York University

  24. AI-Generated Measurements for Identification and Inference with Missing Data: A Weak Shadow Variable Approach

    Feb 17, 2026Hongyu Chen, David Simchi-Levi, Ruoxuan XiongLearning with Missing DataPartial Identification

    Massachusetts Institute of Technology, Cambridge, MA 02139 · Emory University, Atlanta, GA 30322

  25. The Stationarity Bias: Stratified Stress-Testing for Time-Series Imputation in Regulated Dynamical Systems

    Feb 17, 2026Amirreza Dolatpour Fathkouhi, Alireza Namazi, Heman ShakeriContinuous Glucose MonitoringTime Series Imputation

    Department of Computer Science Center for Diabetes Technology University of Virginia Charlottesville, Virginia, USA · School of Data Science Center for Diabetes Technology University of Virginia Charlottesville, Virginia, USA

  26. Learning Representations from Incomplete EHR Data with Dual-Masked Autoencoding

    Feb 16, 2026Xiao Xiang, David Restrepo, Hyewon Jeong +2Learning with Missing DataMasked Autoencoders

    Massachusetts Institute of Technology · École Polytechnique Fédérale de Lausanne · Harvard University +1

  27. Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data

    Feb 1, 2026Yinsong Wang, Shahin ShahrampourNeural Network GeneralizationLearning with Missing Data

    University of Nevada Reno, Reno, NV 89557 USA · Department of Mechanical and Industrial Engineering at Northeastern University, Boston, MA 02115 USA

  28. Linear Regression under Missing or Corrupted Coordinates

    Sep 23, 2025Ilias Diakonikolas, Jelena Diakonikolas, Daniel M. Kane +2Robust RegressionLearning with Missing Data

    University of Wisconsin-Madison · University of California, San Diego · University of California, Davis

  29. 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 DataMulti-View Learning

    Department of Computer Science, University of Iowa, Iowa City, Iowa, USA

  30. Deep learning with missing data

    Apr 21, 2025Tianyi Ma, Tengyao Wang, Richard J. SamworthNeural Network GeneralizationLearning with Missing Data

    Statistical Laboratory, University of Cambridge · Department of Statistics, London School of Economics