Optimal Data-Dependent Hashing for Nearest Neighbor Search
Machine Learning seminar by Alex Andoni
Hosted by Simons Institute for the Theory of Computing
Recording
Abstract
This talk develops optimal hashing methods for approximate nearest-neighbor search by reducing worst-case high-dimensional point sets to random instances. The approach connects the structure of arbitrary datasets with hashing constructions designed for random data. The work is joint with Ilya Razenshteyn.
Topics
Related seminars
SeminarSeminarSeminar
Efficient Reductions for k-Nearest Neighbor Search
Related research
Recording · Nov 30, 2018
Nearest Neighbor Methods I
More on data-dependent hashing
Recording · Aug 31, 2018
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Related research
Recording · Dec 9, 2014