Machine Learning For Estimation

Machine Learning for Estimation is a scholarly research contribution that employs machine learning methodologies to model, predict, or infer temporal parameters or events within a timeline, distinguishing itself by leveraging data-driven algorithms to enhance the accuracy and adaptability of temporal estimation tasks.

124

Publications

8.6K

Citations

399

Authors

256

Institutions

Publications per year

2017–2026

109

Authors

399

  1. SM
    Sendhil Mullainathan

    University of Chicago, United States

    3 publications · 2.1K citations

    3

    h-index

  2. MY
    Mika Ylinen

    University of Vaasa, Finland

    2 publications · 108 citations

    2

    h-index

  3. JL
    Jens Ludwig

    University of Chicago, United States

    2 publications · 203 citations

    2

    h-index

  4. MB
    Marijn Bolhuis

    International Monetary Fund, United States

    2 publications · 22 citations

    2

    h-index

  5. YC
    Yiyang Chen

    Soochow University, China

    2 publications · 87 citations

    2

    h-index

1–5 of 399

Venues

  1. SSRN Electronic Journal

    Repository · 8 publications · 260 citations

    8

    h-index

  2. arXiv (Cornell University)

    Repository · 6 publications · 113 citations

    5

    h-index

  3. Sensors

    3 publications · 84 citations

    3

    h-index

  4. Journal of Optical Communications and Networking

    JOCN · 3 publications · 118 citations

    3

    h-index

  5. RePEc: Research Papers in Economics

    Repository · 3 publications · 71 citations

    3

    h-index

1–5