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Alexandr Andoni
Researcher at Columbia University
Publications - 118
Citations - 7676
Alexandr Andoni is an academic researcher from Columbia University. The author has contributed to research in topics: Upper and lower bounds & Nearest neighbor search. The author has an hindex of 36, co-authored 115 publications receiving 6915 citations. Previous affiliations of Alexandr Andoni include Microsoft & Princeton University.
Papers
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Journal ArticleDOI
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Alexandr Andoni,Piotr Indyk +1 more
TL;DR: An algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O(dn 1c2/+o(1)) and space O(DN + n1+1c2 + o(1) + 1/c2), which almost matches the lower bound for hashing-based algorithm recently obtained.
Proceedings ArticleDOI
Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions
Alexandr Andoni,Piotr Indyk +1 more
TL;DR: An algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O and space O almost matches the lower bound for hashing-based algorithm recently obtained in [27].
Posted Content
Practical and Optimal LSH for Angular Distance
TL;DR: Andoni et al. as mentioned in this paper showed the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent.
Posted Content
Optimal Data-Dependent Hashing for Approximate Near Neighbors
Alexandr Andoni,Ilya Razenshteyn +1 more
TL;DR: In this article, an optimal data-dependent hashing scheme for the approximate near neighbor problem was proposed, which achieves query time O(d n^{\rho+o(1)}) and space O(n 2 + o(1 + dn) for the Euclidean space and approximation factor c>1 for the Hamming space.
Proceedings Article
Practical and optimal LSH for angular distance
TL;DR: This work shows the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent and establishes a fine-grained lower bound for the quality of any LSH family for angular distance.