Simple, Optimal Algorithms for Random Sampling Without Replacement
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Abstract (arXiv)
Consider the fundamental problem of drawing a simple random sample of size k without replacement from [n] := {1, . . . , n}. Although a number of classical algorithms exist for this problem, we construct algorithms that are even simpler, easier to implement, and have optimal space and time complexity.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2021.
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- 2024Scalable Subsampling Inference for Deep Neural Networkscited by 5
- 2024Scalable Subsampling Inference for Deep Neural Networks14 May 2024