Near-Optimal Bayesian Active Learning with Noisy Observations

NIPS, pp. 766-774, 2010.

Cited by: 137|Bibtex|Views22
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perf...More

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