Active Learning From Demonstration

Active Learning From Demonstration is a machine learning framework within the paradigm of Learning From Demonstration that distinguishes itself by actively selecting informative demonstrations through query strategies to optimize learning efficiency and performance.

28

Publications

801

Citations

71

Authors

26

Institutions

Publications per year

2017–2026

16

Authors

71

  1. SN
    Scott Niekum

    The University of Texas at Austin, United States

    5 publications · 172 citations

    5

    h-index

  2. DS
    Daniel S. Brown

    The University of Texas at Austin, United States

    4 publications · 113 citations

    4

    h-index

  3. CC
    Crystal Chao

    Georgia Institute of Technology, United States

    3 publications · 129 citations

    3

    h-index

  4. YC
    Yuchen Cui

    The University of Texas at Austin, United States

    3 publications · 102 citations

    3

    h-index

  5. MÇ
    Maya Çakmak

    University of Washington, United States

    3 publications · 129 citations

    3

    h-index

1–5 of 71

Venues

  1. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 2 publications · 23 citations

    2

    h-index

  2. Adaptive Agents and Multi-Agents Systems

    Conference · 1 publications · 17 citations

    1

    h-index

  3. Pharmacy

    1 publications · 27 citations

    1

    h-index

  4. National Conference on Artificial Intelligence

    Conference · 1 publications · 12 citations

    1

    h-index

  5. CaltechAUTHORS (California Institute of Technology)

    Repository · 1 publications · 53 citations

    1

    h-index

1–5