Active Learning With Gaussian Processes

Active Learning with Gaussian Processes is a machine learning framework that iteratively selects the most informative data points for labeling by leveraging the probabilistic predictions and uncertainty estimates of a Gaussian process model to optimize the training data acquisition process.

97

Publications

5.2K

Citations

269

Authors

131

Institutions

Publications per year

2017–2026

52

Authors

269

  1. AK
    Ashish Kapoor

    Microsoft (United States), United States

    5 publications · 749 citations

    5

    h-index

  2. GH
    Gang Hua

    Microsoft (United States), United States

    3 publications · 184 citations

    3

    h-index

  3. CL
    Chengjiang Long

    Wuhan University, China

    3 publications · 184 citations

    3

    h-index

  4. SS
    Shiliang Sun

    East China Normal University, China

    3 publications · 53 citations

    3

    h-index

  5. AK
    Andreas Krause

    ETH Zurich, Switzerland

    3 publications · 352 citations

    3

    h-index

1–5 of 269

Venues

  1. arXiv (Cornell University)

    Repository · 8 publications · 153 citations

    8

    h-index

  2. Reliability Engineering & System Safety

    5 publications · 164 citations

    5

    h-index

  3. Structural Safety

    3 publications · 299 citations

    3

    h-index

  4. 2

    h-index

  5. International Journal of Computer Vision

    2 publications · 241 citations

    2

    h-index

1–5