Hyperparameter Optimization

Hyperparameter Optimization is the systematic process of selecting optimal hyperparameter values for a machine learning model by exploring the hyperparameter space to maximize model performance on a given task, distinguished from general model training by its focus on tuning fixed structural parameters prior to learning.

2K

Publications

190.5K

Citations

7.3K

Authors

2.2K

Institutions

Publications per year

2017–2026

1.8K

Authors

7.3K

  1. FH
    Frank Hutter

    University of Freiburg, Germany

    28 publications · 5.4K citations

    21

    h-index

  2. AK
    Aaron Klein

    University of Freiburg, Germany

    13 publications · 2.2K citations

    13

    h-index

  3. AT
    Ameet Talwalkar

    University of California, Berkeley, United States

    11 publications · 3.3K citations

    11

    h-index

  4. BB
    Bernd Bischl

    Ludwig-Maximilians-Universität München, Germany

    13 publications · 2.2K citations

    11

    h-index

  5. RP
    Ryan P. Adams

    Harvard University, United States

    10 publications · 8.6K citations

    10

    h-index

1–5 of 7.3K

Venues

  1. arXiv (Cornell University)

    Repository · 257 publications · 27.1K citations

    62

    h-index

  2. IEEE Access

    65 publications · 3.6K citations

    33

    h-index

  3. Sensors

    33 publications · 2K citations

    25

    h-index

  4. Applied Sciences

    36 publications · 1.4K citations

    24

    h-index

  5. Scientific Reports

    28 publications · 1.7K citations

    23

    h-index

1–5