Approximate Selection with Guarantees using Proxies
Summary: Introduces algorithms for approximate selection with statistical guarantees using cheap proxies and limited exact identifications from an oracle. Guarantees target precision or recall with high probability, outperforming prior proxy-based methods—up to 30x improvement on real and synthetic datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Kang (Stanford University)
- 2. Edward Gan (Stanford University)
- 3. Peter Bailis (Stanford University)
- 4. Tatsunori Hashimoto (Stanford University)
- 5. Matei Zaharia (Stanford University)
BibTeX Citation
@article{kang_vldb20,
title = {{Approximate Selection with Guarantees using Proxies}},
author = {Kang, Daniel and Gan, Edward and Bailis, Peter and Hashimoto, Tatsunori and Zaharia, Matei},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {11},
pages = {1990--2003},
doi = {10.14778/3407790.3407804},
url = {https://doi.org/10.14778/3407790.3407804},
year = {2020}
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