A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)

Alexander W. Lee, Stefan Walzer-Goldfeld, Shukry Zablah, Matteo Riondato

AAAI 2022 pp. 12991-12992

doi:10.1609/AAAI.V36I11.21632 /aaai/2022/lee2022aaai-scalable/

Abstract

We present a novel parallel algorithm for drawing balanced samples from large populations. When auxiliary variables about the population units are known, balanced sampling improves the quality of the estimations obtained from the sample. Available algorithms, e.g., the cube method, are inherently sequential, and do not scale to large populations. Our parallel algorithm is based on a variant of the cube method for stratified populations. It has the same sample quality as sequential algorithms, and almost ideal parallel speedup.

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Cite

Text

Lee et al. "A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21632

Markdown

[Lee et al. "A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/lee2022aaai-scalable/) doi:10.1609/AAAI.V36I11.21632

BibTeX

@inproceedings{lee2022aaai-scalable,
  title     = {{A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)}},
  author    = {Lee, Alexander W. and Walzer-Goldfeld, Stefan and Zablah, Shukry and Riondato, Matteo},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12991-12992},
  doi       = {10.1609/AAAI.V36I11.21632},
  url       = {https://mlanthology.org/aaai/2022/lee2022aaai-scalable/}
}