Awesome ML Positive Unlabeled learning

Papers

• Papers \w code
  • Overview
    • Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning(2016)
    • Learning from positive and unlabeled data: a survey(2018)
    • Positive and unlabeled learning algorithms and applications: A survey(2019)
    • Exploring Positive Unlabeled Machine Learning(2021)
  • Cost-sensitive methods
    • Learning Classifiers from Only Positive and Unlabeled Data(2008)[pulearn]
    • Analysis of Learning from Positive and Unlabeled Data(2014)
    • A Modified Logistic Regression for Positive and Unlabeled Learning(2019) [video]
    • DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning(2019)[source]
  • Sample-selection/Two-step methods
    • Positive-unlabeled learning for disease gene identification(2012)
    • A bagging SVM to learn from positive and unlabeled examples(2013) [pulearn]
    • Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative(2019)
    • Improving Positive Unlabeled Learning: Practical AUL Estimation and New Training Method for Extremely Imbalanced Data Sets(2020)
    • PULNS: Positive-Unlabeled Learning with Effective Negative Sample Selector(2021)[slides]

Implementations

• Frameworks
  • Python
    • nnPUlearning
    • pulearn
    • dedpul
  • Spark
    • pu4spark

Tutorials

• An introductory tutorial to the "Learning from Positive and Unlabeled Data" field.
• tinyML Talks Phoenix: Positive Unlabeled Learning for Tiny ML
• Semi-Supervised Classification of Unlabeled Data (PU Learning)

Concepts

• Class prior and label frequency
• Cost-sensitive and sample-selection methods
• Inductive vs Transductive PU learning. a.k.a(?) (Single-training set vs case-control scenario)
• Labelling mechanism
• Assumptions
  • Partial Seperability
  • SCAR(Selected Completely At Random)

Cases/Applications

• Positive Unlabeled Learing, MegaFon, ODS.ai(2020)[RU]