A Scalable Parallel Algorithm for Balanced Sampling

被引：0
|
作者：
Lee, Alexander [1 ]
Walzer-Goldfeld, Stefan [1 ]
Zablah, Shukry [2 ]
Riondato, Matteo [1 ]
机构：
[1] Amherst Coll, Dept Comp Sci, Box 2232, Amherst, MA 01002 USA
[2] Pallet Labs Inc, Boulder, CO USA
来源：
THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2022年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：12991 / 12992
页数：2
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