Universal Bayes Consistency in Metric Spaces

Steve Hanneke, Aryeh Kontorovich, Sivan Sabato, Roi Weiss

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

We extend a recently proposed 1-nearest-neighbor based multiclass learning algorithm and prove that our modification is universally strongly Bayes consistent in all metric spaces admitting any such learner, making it an “optimistically universal” Bayes-consistent learner. This is the first learning algorithm known to enjoy this property; by comparison, the k-NN classifier and its variants are not generally universally Bayes consistent, except under additional structural assumptions, such as an inner product, a norm, finite dimension or a Besicovitch-type property. The metric spaces in which universal Bayes consistency is possible are the “essentially separable” ones-a notion that we define, which is more general than standard separability. The existence of metric spaces that are not essentially separable is widely believed to be independent of the ZFC axioms of set theory. We prove that essential separability exactly characterizes the existence of a universal Bayes-consistent learner for the given metric space. In particular, this yields the first impossibility result for universal Bayes consistency. Taken together, our results completely characterize strong and weak universal Bayes consistency in metric spaces.

Original languageEnglish GB
Pages (from-to)2129-2150
Number of pages22
JournalAnnals of Statistics
Volume49
Issue number4
DOIs
StatePublished - 1 Aug 2021

Keywords

  • Bayes consistency
  • Classification
  • Metric space
  • Nearest neighbor

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Universal Bayes Consistency in Metric Spaces'. Together they form a unique fingerprint.

Cite this