Random Sampling Algorithms

Random Sampling Algorithms is a class of randomized algorithms that employ the process of selecting a subset of elements from a larger population according to a probability distribution to estimate properties or approximate solutions, distinguished by their reliance on stochastic sampling to achieve efficiency or accuracy in computation.

412

Publications

34.6K

Citations

1K

Authors

479

Institutions

Publications per year

2017–2026

130

Authors

1K

  1. RM
    Rajeev Motwani

    Stanford University, United States

    4 publications · 765 citations

    4

    h-index

  2. SC
    Surajit Chaudhuri

    Microsoft (United States), United States

    4 publications · 765 citations

    4

    h-index

  3. VN
    Vivek Narasayya

    Microsoft (United States), United States

    4 publications · 765 citations

    4

    h-index

  4. JS
    Jeffrey Scott Vitter

    Duke University, United States

    4 publications · 2K citations

    4

    h-index

  5. KG
    Karlheinz Gröchenig

    University of Vienna, Austria

    4 publications · 550 citations

    4

    h-index

1–5 of 1K

Venues

  1. arXiv (Cornell University)

    Repository · 16 publications · 433 citations

    13

    h-index

  2. ACM Transactions on Mathematical Software

    TOMS · 5 publications · 1.9K citations

    6

    h-index

  3. Bioinformatics

    6 publications · 749 citations

    6

    h-index

  4. ACM SIGMOD Record

    5 publications · 1.3K citations

    5

    h-index

  5. 5

    h-index

1–5