Beyond disagreement-based agnostic active learning

Chicheng Zhang, Kamalika Chaudhuri

Research output: Contribution to journalConference articlepeer-review

55 Scopus citations


We study agnostic active learning, where the goal is to learn a classifier in a pre-specified hypothesis class interactively with as few label queries as possible, while making no assumptions on the true function generating the labels. The main algorithm for this problem is disagreement-based active learning, which has a high label requirement. Thus a major challenge is to find an algorithm which achieves better label complexity, is consistent in an agnostic setting, and applies to general classification problems. In this paper, we provide such an algorithm. Our solution is based on two novel contributions; first, a reduction from consistent active learning to confidence-rated prediction with guaranteed error, and second, a novel confidence-rated predictor.

Original languageEnglish (US)
Pages (from-to)442-450
Number of pages9
JournalAdvances in Neural Information Processing Systems
Issue numberJanuary
StatePublished - 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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