Active Learning For Inference

Active Learning for Inference is an adaptive methodology within simulation-based inference that strategically selects informative queries or experimental conditions to optimize the efficiency and accuracy of parameter estimation or model discrimination.

112

Publications

5.3K

Citations

337

Authors

156

Institutions

Publications per year

2017–2026

65

Authors

337

  1. KF
    Karl Friston

    University College London, United Kingdom

    8 publications · 1.2K citations

    8

    h-index

  2. GP
    Giovanni Pezzulo

    Institute of Cognitive Sciences and Technologies, Italy

    4 publications · 201 citations

    4

    h-index

  3. JS
    Jeff Schneider

    Carnegie Mellon University, United States

    3 publications · 69 citations

    3

    h-index

  4. TP
    Thomas Parr

    University College London, United Kingdom

    2 publications · 142 citations

    2

    h-index

  5. DK
    Daphne Koller

    Stanford University, United States

    2 publications · 322 citations

    2

    h-index

1–5 of 337

Venues

  1. Reliability Engineering & System Safety

    7 publications · 319 citations

    7

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 4 publications · 79 citations

    4

    h-index

  3. Expert Systems with Applications

    4 publications · 175 citations

    4

    h-index

  4. arXiv (Cornell University)

    Repository · 4 publications · 102 citations

    4

    h-index

  5. Neurocomputing

    3 publications · 77 citations

    3

    h-index

1–5