Fast and Bayes-Consistent Nearest neighbors

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Research on nearest-neighbor methods tends to focus somewhat dichotomously either on the statistical or the computational aspects —either on, say, Bayes consistency and rates of convergence or on techniques for speeding up the proximity search. This paper aims at bridging these realms: to reap the advantages of fast evaluation time while maintaining Bayes consistency, and further without sacrificing too much in the risk decay rate. We combine the locality-sensitive hashing (LSH) technique with a novel missing-mass argument to obtain a fast and Bayes-consistent classifier. Our algorithm’s prediction runtime compares favorably against state of the art approximate NN methods, while maintaining Bayes-consistency and attaining rates comparable to minimax. On samples of size n in Rd, our pre-processing phase has runtime O(dnlogn), while the evaluation phase has runtime O(dlogn) per query point.
Original languageEnglish
Title of host publicationThe 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]
EditorsSilvia Chiappa, Roberto Calandra
Number of pages11
StatePublished - 2020

Publication series

NameProceedings of Machine Learning Research


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