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Search Improves Label for Active Learning
lib:5ad4aa4b55942a34 (v1.0.0)
Authors: Alina Beygelzimer,Daniel Hsu,John Langford,Chicheng Zhang
Where published: NeurIPS 2016 12
ArXiv: 1602.07265
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1602.07265v2
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.
Where published: NeurIPS 2016 12
ArXiv: 1602.07265
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1602.07265v2
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.
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