Deferral Decisions

Deferral Decisions is a machine learning paradigm that characterizes the process by which a model strategically abstains from making a prediction in uncertain or ambiguous instances, instead referring such cases to an external agent or delayed processing to enhance overall system reliability and accuracy.

61

Publications

2.3K

Citations

238

Authors

119

Institutions

Publications per year

2017–2026

15

Variants

Learning With Deferral

Authors

238

  1. BC
    Brian Custer

    University of California, San Francisco, United States

    6 publications · 261 citations

    6

    h-index

  2. MG
    Mindy Goldman

    Canadian Blood Services, Canada

    5 publications · 124 citations

    5

    h-index

  3. WF
    Wenli Fan

    Canadian Blood Services, Canada

    4 publications · 103 citations

    4

    h-index

  4. SF
    Sheila F. O’Brien

    Canadian Blood Services, Canada

    4 publications · 103 citations

    4

    h-index

  5. MP
    Michael P. Busch

    University of California, San Francisco, United States

    3 publications · 175 citations

    3

    h-index

1–5 of 238

Venues

  1. Transfusion

    24 publications · 1K citations

    20

    h-index

  2. Transfusion Medicine

    2 publications · 25 citations

    2

    h-index

  3. Vox Sanguinis

    2 publications · 36 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 81 citations

    2

    h-index

  5. Deep Blue (University of Michigan)

    Repository · 1 publications · 10 citations

    1

    h-index

1–5