text: 227,98,1136535682573320192,1104703454,Francesco Silvestri,"New paper! “Fair Near Neighbor Search: Independent Range Sampling in High Dimensions“ by M. Aumüller @RasmusPagh1 and myself: <LINK>. Given a query q, we show how to evenly sample near neighbors of q in a high dimensional space. (1/2) It is ""fair"" in the sense that all points in the neighborhood have equal opportunity. Just using LSH is not enough: the closest point is more likely to be returned! We show how to make any LSH fair and also give a nearly-linear space for inner product. (2/2)",https://arxiv.org/abs/1906.01859,"Similarity search is a fundamental algorithmic primitive, widely used in many computer science disciplines. There are several variants of the similarity search problem, and one of the most relevant is the $r$-near neighbor ($r$-NN) problem: given a radius $r>0$ and a set of points $S$, construct a data structure that, for any given query point $q$, returns a point $p$ within distance at most $r$ from $q$. In this paper, we study the $r$-NN problem in the light of fairness. We consider fairness in the sense of equal opportunity: all points that are within distance $r$ from the query should have the same probability to be returned. In the low-dimensional case, this problem was first studied by Hu, Qiao, and Tao (PODS 2014). Locality sensitive hashing (LSH), the theoretically strongest approach to similarity search in high dimensions, does not provide such a fairness guarantee. 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 and returned by the query. Specifically, we first propose a black-box approach that, given any LSH scheme, constructs a data structure for uniformly sampling points in the neighborhood of a query. Then, we develop a data structure for fair similarity search under inner product that requires nearly-linear space and exploits locality sensitive filters. The paper concludes with an experimental evaluation that highlights (un)fairness in a recommendation setting on real-world datasets and discusses the inherent unfairness introduced by solving other variants of the problem. ",Fair Near Neighbor Search: Independent Range Sampling in High Dimensions,2,"['New paper! “Fair Near Neighbor Search: Independent Range Sampling in High Dimensions“ by M. Aumüller @RasmusPagh1 and myself: <LINK>. Given a query q, we show how to evenly sample near neighbors of q in a high dimensional space. (1/2)', 'It is ""fair"" in the sense that all points in the neighborhood have equal opportunity. Just using LSH is not enough: the closest point is more likely to be returned! We show how to make any LSH fair and also give a nearly-linear space for inner product. (2/2)']",19,06,493