Adaptivity to local smoothness and dimension in kernel regression

Samory Kpotufe, Vikas K. Garg

Research output: Contribution to journalConference articleScientificpeer-review

15 Citations (Scopus)

Abstract

We present the first result for kernel regression where the procedure adapts locally at a point x to both the unknown local dimension of the metric space χ and the unknown Hölder-continuity of the regression function at x. The result holds with high probability simultaneously at all points x in a general metric space χ of unknown structure.

Original languageEnglish
JournalADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventIEEE Conference on Neural Information Processing Systems - Lake Tahoe, United States
Duration: 5 Dec 201310 Dec 2013

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