Machine Learning For Sorting

Machine Learning for Sorting is a computational methodology within the field of machine learning that specifically focuses on developing and applying data-driven models and algorithms to infer optimal or near-optimal ordering strategies from input data, distinguishing it from traditional algorithmic sorting approaches by leveraging learned patterns rather than predefined comparison rules.

53

Publications

1.8K

Citations

194

Authors

63

Institutions

Publications per year

2017–2026

26

Authors

194

  1. XL
    Xiaoming Li

    Peking University, China

    3 publications · 163 citations

    3

    h-index

  2. MJ
    María Jesús Garzarán

    University of Illinois Urbana-Champaign, United States

    3 publications · 163 citations

    3

    h-index

  3. DP
    David Padua

    University of Illinois Urbana-Champaign, United States

    3 publications · 163 citations

    3

    h-index

  4. PK
    Pushmeet Kohli

    Microsoft Research (United Kingdom), United Kingdom

    2 publications · 180 citations

    2

    h-index

  5. ST
    Satoshi Tezuka

    The University of Tokyo, Japan

    2 publications · 94 citations

    2

    h-index

1–5 of 194

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 99 citations

    5

    h-index

  2. Biosystems Engineering

    3 publications · 166 citations

    3

    h-index

  3. European Journal of Operational Research

    2 publications · 88 citations

    2

    h-index

  4. Computational Linguistics

    2 publications · 94 citations

    2

    h-index

  5. Applied Sciences

    2 publications · 41 citations

    2

    h-index

1–5