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)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,792 | Querying in the Age of Graph Databases and Knowledge Graphs | 2021 | SIGMOD | 5.325937e-05 |
| 6,462 | Algorithmic Techniques for Independent Query Sampling | 2022 | PODS | 5.0536751e-05 |
| 8,610 | Efficient Dynamic Weighted Set Sampling and Its Extension | 2024 | VLDB | 4.4853485e-05 |
| 9,758 | Practical Dynamic Extension for Sampling Indexes | 2023 | SIGMOD | 4.2879116e-05 |
| 10,960 | FairHash: A Fair and Memory/Time-efficient Hashmap | 2024 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,203 | Independent Range Sampling | 2014 | PODS | 9.2981095e-05 |
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