Efficient and parsimonious agnostic active learning

Tzu Kuo Huang, Alekh Agarwal, Daniel Hsu, John Langford, Robert E. Schapire

Research output: Contribution to journalConference article

9 Scopus citations

Abstract

We develop a new active learning algorithm for the streaming setting satisfying three important properties:1) It provably works for any classifier representation and classification problem including those with severe noise. 2) It is efficiently implementable with an ERM oracle. 3) It is more aggressive than all previous approaches satisfying 1 and 2. To do this, we create an algorithm based on a newly defined optimization problem and analyze it. We also conduct the first experimental analysis of all efficient agnostic active learning algorithms, evaluating their strengths and weaknesses in different settings.

Original languageEnglish (US)
Pages (from-to)2755-2763
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2015-January
StatePublished - Jan 1 2015
Externally publishedYes
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Huang, T. K., Agarwal, A., Hsu, D., Langford, J., & Schapire, R. E. (2015). Efficient and parsimonious agnostic active learning. Advances in Neural Information Processing Systems, 2015-January, 2755-2763.