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Learning to Sample: Counting with Complex Queries

Summary: Two-phase learning-to-sample: sample to train a classifier, then guide stratified counting for complex queries. Theory derives optimal stratification; empirically compares against quantification and weighted/stratified sampling across real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12243
Venue
VLDB
Year
2020
Pagerank
4.7890519e-05
Overall Rank
7,251 | 49.56%
DOI
10.14778/3368289.3368302

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