Human-in-the-loop Learning

Human-In-The-Loop Learning is a machine learning paradigm in which human agents are actively integrated into the computational learning process to provide feedback, guidance, or intervention that directly informs model updates, distinguishing it from fully autonomous or unsupervised learning approaches by embedding human expertise, intent, or judgment as essential components of policy or behavior refinement.

315

Publications

20.1K

Citations

1.1K

Authors

435

Institutions

Publications per year

2017–2026

238

Authors

1.1K

  1. AH
    Andreas Holzinger

    Medical University of Graz, Austria

    7 publications · 1.4K citations

    7

    h-index

  2. EM
    Eduardo Mosqueira-Rey

    Universidade da Coruña, Spain

    4 publications · 899 citations

    5

    h-index

  3. AP
    Aditya Parameswaran

    Stanford University, United States

    3 publications · 187 citations

    4

    h-index

  4. EH
    Elena Hernández-Pereira

    Universidade da Coruña, Spain

    3 publications · 881 citations

    4

    h-index

  5. DX
    Doris Xin

    University of California, Berkeley, United States

    3 publications · 187 citations

    4

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 24 publications · 667 citations

    15

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 10 publications · 575 citations

    10

    h-index

  3. 5

    h-index

  4. 3

    h-index

  5. IEEE Transactions on Human-Machine Systems

    3 publications · 185 citations

    3

    h-index

1–5