Dataset Aggregation

Dataset Aggregation is a method in imitation learning that iteratively expands a training dataset by incorporating new expert demonstrations collected from regions of the state space where the learned policy exhibits behavioral divergence from optimal actions.

121

Publications

8.1K

Citations

596

Authors

310

Institutions

Publications per year

2017–2026

79

Variants

Dagger

Authors

596

  1. NH
    Nhat-Lan H. Nguyen

    University of Virginia, United States

    2 publications · 37 citations

    2

    h-index

  2. LG
    Lauren Gardner

    Johns Hopkins University, United States

    2 publications · 37 citations

    2

    h-index

  3. GH
    Gaige Hunter Kerr

    George Washington University, United States

    2 publications · 37 citations

    2

    h-index

  4. JG
    Juan Guardiola

    University of Louisville, United States

    2 publications · 48 citations

    2

    h-index

  5. XL
    Xiaotong Li

    Xidian University, China

    2 publications · 150 citations

    2

    h-index

1–5 of 596

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 89 citations

    5

    h-index

  2. Scientific Data

    3 publications · 211 citations

    3

    h-index

  3. PubMed

    Repository · 2 publications · 553 citations

    2

    h-index

  4. The American Statistician

    2 publications · 34 citations

    2

    h-index

  5. IEEE Transactions on Biomedical Engineering

    2 publications · 92 citations

    2

    h-index

1–5