Search improves label for active learning

Alina Beygelzimer, Daniel Hsu, John Langford, Chicheng Zhang

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations

Abstract

We investigate active learning with access to two distinct oracles: LABEL (which is standard) and SEARCH (which is not). The SEARCH oracle models the situation where a human searches a database to seed or counterexample an existing solution. SEARCH is stronger than LABEL while being natural to implement in many situations. We show that an algorithm using both oracles can provide exponentially large problem-dependent improvements over LABEL alone.

Original languageEnglish (US)
Pages (from-to)3350-3358
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - 2016
Externally publishedYes
Event30th Annual Conference on Neural Information Processing Systems, NIPS 2016 - Barcelona, Spain
Duration: Dec 5 2016Dec 10 2016

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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