Decision Tree Complexity

Decision Tree Complexity is a computational complexity measure that quantifies the minimum number of queries or node evaluations required in the worst case by a decision tree to compute a Boolean function, distinguishing it from other complexity measures by its focus on the depth or structure of adaptive query strategies for function evaluation.

221

Publications

15.8K

Citations

406

Authors

209

Institutions

Publications per year

2017–2026

33

Authors

406

  1. MM
    Mikhail Moshkov

    King Abdullah University of Science and Technology, Saudi Arabia

    8 publications · 178 citations

    8

    h-index

  2. AC
    Andrew Chi-Chih Yao

    Princeton University, United States

    7 publications · 382 citations

    7

    h-index

  3. RC
    Richard Cleve

    University of Calgary, Canada

    5 publications · 1.1K citations

    5

    h-index

  4. YM
    Yishay Mansour

    Tel Aviv University, Israel

    5 publications · 573 citations

    5

    h-index

  5. IC
    Igor Chikalov

    King Abdullah University of Science and Technology, Saudi Arabia

    5 publications · 114 citations

    5

    h-index

1–5 of 406

Venues

  1. SIAM Journal on Computing

    14 publications · 1.2K citations

    13

    h-index

  2. Machine Learning

    8 publications · 1.5K citations

    10

    h-index

  3. Journal of the ACM

    JACM · 8 publications · 1.2K citations

    8

    h-index

  4. arXiv (Cornell University)

    Repository · 7 publications · 140 citations

    7

    h-index

  5. Fundamenta Informaticae

    6 publications · 146 citations

    6

    h-index

1–5