Adaptivity to local smoothness and dimension in kernel regression

Samory Kpotufe, Vikas K. Garg

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


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 languageAmerican English
JournalAdvances in Neural Information Processing Systems
StatePublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: Dec 5 2013Dec 10 2013

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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