mlsanityData to 7 Oct 2026

Learning from data with structured missingness

arXiv 2304.01429 ↗First posted 4 Apr 2023stat.ML cs.LGcited by 24

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Abstract (arXiv)

Missing data are an unavoidable complication in many machine learning tasks. When data are `missing at random' there exist a range of tools and techniques to deal with the issue. However, as machine learning studies become more ambitious, and seek to learn from ever-larger volumes of heterogeneous data, an increasingly encountered problem arises in which missing values exhibit an association or structure, either explicitly or implicitly. Such `structured missingness' raises a range of challenges that have not yet been systematically addressed, and presents a fundamental hindrance to machine learning at scale. Here, we outline the current literature and propose a set of grand challenges in learning from data with structured missingness.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 25 of its 110 references that are in the map. First posted on arXiv 4 Apr 2023.

Builds on

25 of 110 references.

References · 25

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13 more
  1. 2021Contrastive Learning for Fair Representationscited by 34
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  3. 2020Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictionscited by 20
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 9; 2026: 8.

24 papers.

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  1. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  2. 2024Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Valuescited by 10
  3. 2025Glocal Information Bottleneck for Time Series Imputationcited by 9
  4. 2025LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputationcited by 8
  5. 2025Deep Learning in Concealed Dense Predictioncited by 6
  6. 2024How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputationcited by 5
  7. 2025CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputationcited by 3
  8. 2023A Complete Characterisation of Structured Missingnesscited by 2
  9. 2025Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approachescited by 2
  10. 2025Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promisescited by 2
  11. 2025One Pipeline, Many Transformers: Pattern-Specific Imputation Specialists for Tabular Missing Datacited by 2
  12. 2026Missingness-aware Data Imputation via AI-powered Bayesian Generative Modelingcited by 2
12 more
  1. 2023CSAI: Conditional Self-Attention Imputation for Healthcare Time-seriescited by 1
  2. 2024Latent variable model for high-dimensional point process with structured missingnesscited by 1
  3. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  4. 2026Federated Imputation under Heterogeneous Feature Spacescited by 1
  5. 2025Missing-Modality-Aware Graph Neural Network for Cancer Classificationcited by 0
  6. 2025Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulationcited by 0
  7. 2026Nearly Optimal Bayesian Inference for Structural Missingnesscited by 0
  8. 2026Theoretical Analysis of Measure Consistency Regularization for Partially Observed Datacited by 0
  9. 2026Multi-Level Causal Embeddingscited by 0
  10. 2026FILLER: Feature Imputation via Latent Location Exploration and Retrievalcited by 0
  11. 2026CRS-Triage: Confidence- and Reliability-Aware Selective Triage under Incomplete Clinical Evidencecited by 0
  12. 2026A Time-Aware Bag-of-Receptive-Fields for Interpretable Irregular Time Series Classificationcited by 0