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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
12431
Venue
VLDB
Year
2020
Pagerank
5.7178054e-05
Overall Rank
7,048 | 51.65%
DOI
10.14778/3368289.3368302

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{walenz_vldb20,
        title = {{Learning to Sample: Counting with Complex Queries}},
        author = {Walenz, Brett and Sintos, Stavros and Roy, Sudeepa and Yang, Jun},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {3},
        pages = {389--401},
        doi = {10.14778/3368289.3368302},
        url = {https://doi.org/10.14778/3368289.3368302},
        year = {2020}
}

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