Randomized Reductions

Randomized Reductions is a computational method in randomized complexity theory that transforms one problem into another with bounded error probability, distinguishing it from deterministic reductions by incorporating randomness to achieve correctness with high probability.

39

Publications

2.4K

Citations

57

Authors

41

Institutions

Publications per year

2017–2026

4

Authors

57

  1. PR
    Pankaj Rohatgi

    IBM (United States), United States

    4 publications · 67 citations

    4

    h-index

  2. SP
    Seth Pettie

    University of Michigan, United States

    3 publications · 145 citations

    3

    h-index

  3. DM
    Daniele Micciancio

    University of California San Diego, United States

    2 publications · 296 citations

    2

    h-index

  4. SD
    Shaddin Dughmi

    University of Southern California, United States

    2 publications · 76 citations

    2

    h-index

  5. EP
    Ely Porat

    Bar-Ilan University, Israel

    2 publications · 127 citations

    2

    h-index

1–5 of 57

Venues

  1. SIAM Journal on Computing

    6 publications · 427 citations

    6

    h-index

  2. Journal of Computer and System Sciences

    4 publications · 124 citations

    4

    h-index

  3. IEEE Transactions on Information Theory

    2 publications · 125 citations

    2

    h-index

  4. Symposium on Discrete Algorithms

    Conference · 1 publications · 67 citations

    1

    h-index

  5. Quantum

    1 publications · 23 citations

    1

    h-index

1–5