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Learning from positive and unlabeled data: a survey

arXiv 1811.04820 ↗First posted 12 Nov 2018cs.LG stat.MLcited by 219

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Abstract (arXiv)

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.

Key facts. Cited by 219 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 23; 2022: 45; 2023: 42; 2024: 41; 2025: 36; 2026: 32.

219 papers.

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  1. 2021Generalized Out-of-Distribution Detection: A Surveycited by 802
  2. 2021A Survey on Deep Semi-supervised Learningcited by 251
  3. 2021Multi-Label Learning from Single Positive Labelscited by 81
  4. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 78
  5. 2021Recovering the Unbiased Scene Graphs from the Biased Onescited by 71
  6. 2021Explainable Deep Few-shot Anomaly Detection with Deviation Networkscited by 69
  7. 2023Multiscale Positive-Unlabeled Detection of AI-Generated Textscited by 61
  8. 2021Mixture Proportion Estimation and PU Learning: A Modern Approachcited by 52
  9. 2022Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspectivecited by 38
  10. 2022Domain Adaptation under Open Set Label Shiftcited by 32
  11. 2023RLSbench: Domain Adaptation Under Relaxed Label Shiftcited by 30
  12. 2023Learning Off-Road Terrain Traversability with Self-Supervisions Onlycited by 27
38 more
  1. 2024Large Language Models for Anomaly and Out-of-Distribution Detection: A Surveycited by 27
  2. 2021Multi-label Classification with Partial Annotations using Class-aware Selective Losscited by 25
  3. 2022OpenTAL: Towards Open Set Temporal Action Localizationcited by 23
  4. 2022Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learningcited by 21
  5. 2022Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rayscited by 21
  6. 2022ScaTE: A Scalable Framework for Self-Supervised Traversability Estimation in Unstructured Environmentscited by 21
  7. 2022Spatial Consistency Loss for Training Multi-Label Classifiers from Single-Label Annotationscited by 17
  8. 2022A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphscited by 15
  9. 2022On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classificationcited by 15
  10. 2022Adaptive novelty detection with false discovery rate guaranteecited by 15
  11. 2024A Short Survey on Importance Weighting for Machine Learningcited by 15
  12. 2021Offline Inverse Reinforcement Learningcited by 14
  13. 2022Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completioncited by 13
  14. 2022Domain Adaptation under Missingness Shiftcited by 13
  15. 2023SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Predictioncited by 13
  16. 2024Decoupling the Class Label and the Target Concept in Machine Unlearningcited by 12
  17. 2023Weakly Supervised AUC Optimization: A Unified Partial AUC Approachcited by 11
  18. 2023Online Label Shift: Optimal Dynamic Regret meets Practical Algorithmscited by 11
  19. 2021Sparse Object-level Supervision for Instance Segmentation with Pixel Embeddingscited by 10
  20. 2022Fairness-aware Model-agnostic Positive and Unlabeled Learningcited by 10
  21. 2023Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trendscited by 10
  22. 2024On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learningcited by 10
  23. 2024A Comprehensive Survey on Inverse Constrained Reinforcement Learning: Definitions, Progress and Challengescited by 10
  24. 2022Unsupervised Learning under Latent Label Shiftcited by 9
  25. 2022Personalized Reward Learning with Interaction-Grounded Learning (IGL)cited by 9
  26. 2022SPADE: Semi-supervised Anomaly Detection under Distribution Mismatchcited by 9
  27. 2023Mixture Proportion Estimation Beyond Irreducibilitycited by 9
  28. 2023A Unified Approach to Count-Based Weakly-Supervised Learningcited by 9
  29. 2021Quantifying disparities in intimate partner violence: a machine learning method to correct for underreportingcited by 8
  30. 2022Positive Unlabeled Contrastive Learningcited by 8
  31. 2022Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimizationcited by 8
  32. 2023Improving Behavioural Cloning with Positive Unlabeled Learningcited by 8
  33. 2023When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detectioncited by 8
  34. 2022Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learningcited by 7
  35. 2023Positive Label Is All You Need for Multi-Label Classificationcited by 7
  36. 2024DALLMi: Domain Adaption for LLM-based Multi-label Classifiercited by 7
  37. 2022Open challenges for Machine Learning based Early Decision-Making researchcited by 6
  38. 2022Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varietiescited by 6

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