Scalable nonlinear learning with adaptive polynomial expansions

Alekh Agarwal, Alina Beygelzimer, Daniel Hsu, John Langford, Matus Telgarsky

Research output: Contribution to journalConference articlepeer-review

Abstract

Can we effectively learn a nonlinear representation in time comparable to linear learning? We describe a new algorithm that explicitly and adaptively expands higher-order interaction features over base linear representations. The algorithm is designed for extreme computational efficiency, and an extensive experimental study shows that its computation/prediction tradeoff ability compares very favorably against strong baselines.

Original languageEnglish (US)
Pages (from-to)2051-2059
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume3
Issue numberJanuary
StatePublished - Jan 1 2014
Externally publishedYes
Event28th Annual Conference on Neural Information Processing Systems 2014, NIPS 2014 - Montreal, Canada
Duration: Dec 8 2014Dec 13 2014

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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