A Scalable Parallel Algorithm for Balanced Sampling (Student Abstract)
Keywords:Sampling, Parallel Algorithms, Random Sampling, Big Data, Scalable Algorithms
AbstractWe 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.
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
AAAI Student Abstract and Poster Program