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.
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2017–2026
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Connelly Barnes, Eli Shechtman, Adam Finkelstein et al. · ACM Transactions on Graphics · 2009 · 2.9K citations
Sudipto Guha, Rajeev Rastogi, Kyuseok Shim · 1998 · 1.9K citations
Random sampling with a reservoir
Jeffrey Scott Vitter · ACM Transactions on Mathematical Software · 1985 · 1.8K citations · Full text
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Stanford University
Stanford, United States
17 h-index
28 publications
6.7K citations
Carnegie Mellon University
Pittsburgh, United States
9 h-index
14 publications
924 citations
Princeton University
Princeton, United States
8 h-index
18 publications
8.1K citations
University of California, Berkeley
Berkeley, United States
7 h-index
9 publications
858 citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
8 publications
282 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
6 h-index
12 publications
1.7K citations
Rensselaer Polytechnic Institute
Troy, United States
6 h-index
10 publications
663 citations

Centre National de la Recherche Scientifique
Paris, France
5 h-index
11 publications
348 citations
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Repository · 16 publications · 433 citations
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TOMS · 5 publications · 1.9K citations
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6 publications · 749 citations
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5 publications · 1.3K citations
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TPAMI · 5 publications · 872 citations
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