Interactive Preference Learning

Interactive Preference Learning is a machine learning framework that iteratively elicits and models user preferences through active interaction, distinguishing itself by integrating real-time feedback into the optimization process to align decision outcomes with subjective human values in complex, multi-objective environments.

21

Publications

816

Citations

66

Authors

43

Institutions

Publications per year

2017–2026

9

Authors

66

  1. IG
    Iryna Gurevych

    Technische Universität Darmstadt, Germany

    2 publications · 49 citations

    2

    h-index

  2. ME
    Martha E. Pollack

    University of Michigan, United States

    2 publications · 73 citations

    2

    h-index

  3. CM
    Christian M. Meyer

    Technische Universität Darmstadt, Germany

    2 publications · 49 citations

    2

    h-index

  4. YG
    Yang Gao

    Technische Universität Darmstadt, Germany

    2 publications · 49 citations

    2

    h-index

  5. HF
    Hélène Fargier

    Université Toulouse III - Paul Sabatier, France

    1 publications · 10 citations

    1

    h-index

1–5 of 66

Venues

  1. Machine Learning

    1 publications · 211 citations

    1

    h-index

  2. International Journal of Artificial Intelligence Tools

    IJAIT · 1 publications · 26 citations

    1

    h-index

  3. International Conference on Machine Learning

    Conference · 1 publications · 25 citations

    1

    h-index

  4. 1

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 10 citations

    1

    h-index

1–5