Jun 5, 2019 · We propose efficient data structures for r-NN where all points in S that are near q have the same probability to be selected and returned by the query.
We start with a simple data structure for the r-near neighbor sam- pling problem in high dimensions that leverages on LSH: with high probability, for each given ...
Jun 14, 2020 · To address this, we propose efficient data structures for r-NN where all points in S that are near q have the same probability to be selected ...
This is a joint version containing minor revisions of the work “Fair near neighbor search: Independent range sampling in high dimensions”, pub- lished in PODS' ...
[PDF] Sampling a Near Neighbor in High Dimensions - Sariel Har-Peled
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Jul 25, 2022 · In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities: all points that are within ...
Individual fairness: every neighbor has the same chance of being reported. ❑ Remove the bias inherent in the NN data structure (also for the downstream tasks).
Supplemental Material for Fair Near Neighbor Search: Independent Range Sampling in High Dimensions. Requirements. Python 3, pandas, seaborn. Recommended ...
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Jun 17, 2021 · In this paper, we study the r-NN problem in the light of individual fairness and providing equal opportunities.
Sep 11, 2024 · In this work, we show that LSH based algorithms can be made fair, without a significant loss in efficiency. We propose several efficient data ...
This work shows that LSH based algorithms can be made fair, without a significant loss in efficiency, and proposes several efficient data structures for the ...