# Simple, Optimal Algorithms for Random Sampling Without Replacement

arXiv: https://arxiv.org/abs/2104.05091 · HTML: https://mlsanity.com/paper/2104.05091/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2021.

## Built on by (most cited first, 1 of 1)

- [Scalable Subsampling Inference for Deep Neural Networks](https://mlsanity.com/paper/2405.08276/) (2024)

## 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.