Constraint-based Structure Learning

Constraint-Based Structure Learning is a method for inferring the structure of a Directed Acyclic Graph by identifying conditional independence relationships among variables to constrain the space of possible graphical models.

45

Publications

2.5K

Citations

105

Authors

59

Institutions

Publications per year

2017–2026

15

Authors

105

  1. MS
    Marco Scutari

    Dalle Molle Institute for Artificial Intelligence Research, Switzerland

    4 publications · 238 citations

    4

    h-index

  2. MH
    Marloes H. Maathuis

    ETH Zurich, Switzerland

    3 publications · 496 citations

    3

    h-index

  3. SA
    Srinivas Aluru

    Iowa State University, United States

    2 publications · 23 citations

    2

    h-index

  4. CW
    Chris Weikart

    Carnegie Mellon University, United States

    2 publications · 67 citations

    2

    h-index

  5. QJ
    Qiang Ji

    Rensselaer Polytechnic Institute, United States

    2 publications · 371 citations

    2

    h-index

1–5 of 105

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 584 citations

    6

    h-index

  2. Pattern Recognition

    1 publications · 119 citations

    1

    h-index

  3. Journal of Statistical Software

    JSS · 1 publications · 149 citations

    1

    h-index

  4. Applied Artificial Intelligence

    AAI · 1 publications · 47 citations

    1

    h-index

  5. Probabilistic Graphical Models

    Conference · 1 publications · 12 citations

    1

    h-index

1–5