A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)

Authors

  • Alexander Lee Amherst College
  • Stefan Walzer-Goldfeld Amherst College
  • Shukry Zablah Pallet Labs Inc
  • Matteo Riondato Amherst College

DOI:

https://doi.org/10.1609/aaai.v36i11.21632

Keywords:

Sampling, Parallel Algorithms, Random Sampling, Big Data, Scalable Algorithms

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.
AAAI-22 Proceedings Cover

Downloads

Published

2022-06-28

How to Cite

Lee, A., Walzer-Goldfeld, S., Zablah, S., & Riondato, M. (2022). A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12991-12992. https://doi.org/10.1609/aaai.v36i11.21632

Issue

Vol. 36 No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program