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Fair Near Neighbor Search: Independent Range Sampling in High Dimensions

Summary: Fair r-near-neighbor search guaranteeing equal-opportunity: every point within radius r has identical selection probability. Provides a black-box reduction turning any LSH into a uniform neighborhood sampler and a nearly-linear-space inner-product data structure using locality-sensitive filters, with experiments exposing LSH-induced unfairness. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1811
Venue
PODS
Year
2020
Pagerank
7.2463734e-05
Overall Rank
3,623 | 75.15%
DOI
10.1145/3375395.3387648

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aumuller_pods20,
        address = {New York, NY, USA},
        series = {{PODS} '20},
        title = {{Fair Near Neighbor Search: Independent Range Sampling in High Dimensions}},
        url = {https://dl.acm.org/doi/10.1145/3375395.3387648},
        doi = {10.1145/3375395.3387648},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Aumüller, Martin and Pagh, Rasmus and Silvestri, Francesco},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 7 of 7 citing papers.

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Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
2,443 Independent Range Sampling 2014 PODS 8.5754434e-05
6,583 Distance-Sensitive Hashing 2018 PODS 5.8359922e-05
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