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no code implementations • 8 May 2022 • Harsha Vardhan Simhadri, George Williams, Martin Aumüller, Matthijs Douze, Artem Babenko, Dmitry Baranchuk, Qi Chen, Lucas Hosseini, Ravishankar Krishnaswamy, Gopal Srinivasa, Suhas Jayaram Subramanya, Jingdong Wang

The outcome of the competition was ranked leaderboards of algorithms in each track based on recall at a query throughput threshold.

no code implementations • 6 Jul 2021 • Matti Karppa, Martin Aumüller, Rasmus Pagh

We present an algorithm called Density Estimation from Approximate Nearest Neighbors (DEANN) where we apply Approximate Nearest Neighbor (ANN) algorithms as a black box subroutine to compute an unbiased KDE.

1 code implementation • 26 Jan 2021 • Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, Francesco Silvestri

Given a set of points $S$ and a radius parameter $r>0$, the $r$-near neighbor ($r$-NN) problem asks for a data structure that, given any query point $q$, returns a point $p$ within distance at most $r$ from $q$.

no code implementations • 18 Aug 2020 • Martin Aumüller, Anders Bourgeat, Jana Schmurr

This paper describes two locally-differential private algorithms for releasing user vectors such that the Jaccard similarity between these vectors can be efficiently estimated.

no code implementations • 17 Jul 2019 • Martin Aumüller, Matteo Ceccarello

This paper reconsiders common benchmarking approaches to nearest neighbor search.

2 code implementations • 28 Jun 2019 • Martin Aumüller, Tobias Christiani, Rasmus Pagh, Michael Vesterli

We describe a novel synthetic data set that is difficult to solve for almost all existing nearest neighbor search approaches, and for which PUFFINN significantly outperform previous methods.

Data Structures and Algorithms Computational Geometry

1 code implementation • 5 Jun 2019 • Martin Aumüller, Rasmus Pagh, Francesco Silvestri

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 $q$.

2 code implementations • 15 Jul 2018 • Martin Aumüller, Erik Bernhardsson, Alexander Faithfull

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms.

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