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)
Incoming Non-self Citations Over Time
Authors
- 1. Brett Walenz
- 2. Stavros Sintos
- 3. Sudeepa Roy
- 4. Jun Yang
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,703 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 5.9793615e-05 |
| 6,565 | Toward Interpretable and Actionable Data Analysis with Explanations and Causality | 2022 | VLDB | 5.0033542e-05 |
| 6,724 | Combining Aggregation and Sampling (Nearly) Optimally for Approximate Query Processing | 2021 | SIGMOD | 4.9449472e-05 |
| 8,383 | Consistent and Flexible Selectivity Estimation for High-Dimensional Data | 2021 | SIGMOD | 4.5261239e-05 |
| 9,487 | Spatial Query Optimization With Learning | 2024 | VLDB | 4.3300131e-05 |
| 10,491 | FAAQP: Fast and Accurate Approximate Query Processing based on Bitmap-augmented Sum-Product Network | 2025 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 14 of 14 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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