Scalable Regression

Scalable Regression is a machine learning methodology that enables the efficient estimation of regression models on large-scale datasets by employing computational techniques that maintain statistical accuracy while significantly reducing time and resource complexity.

54

Publications

2.2K

Citations

154

Authors

75

Institutions

Publications per year

2017–2026

28

Authors

154

  1. MW
    Michael W. Mahoney

    University of California, Berkeley, United States

    2 publications · 29 citations

    2

    h-index

  2. BC
    Brian Cantwell

    Stanford University, United States

    2 publications · 110 citations

    2

    h-index

  3. GM
    Gunasekaran Manogaran

    University of California, Davis, United States

    2 publications · 145 citations

    2

    h-index

  4. MA
    M. Arif Karabeyoğlu

    Stanford University, United States

    2 publications · 107 citations

    2

    h-index

  5. DL
    Daphne Lopez

    Vellore Institute of Technology University, India

    2 publications · 145 citations

    2

    h-index

1–5 of 154

Venues

  1. Journal Of Big Data

    2 publications · 52 citations

    2

    h-index

  2. Journal of Propulsion and Power

    2 publications · 107 citations

    2

    h-index

  3. arXiv (Cornell University)

    Repository · 2 publications · 29 citations

    2

    h-index

  4. International Journal of Advanced Intelligence Paradigms

    IJAIP · 2 publications · 145 citations

    2

    h-index

  5. 1

    h-index

1–5