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Near-Optimal Bayesian Active Learning with Noisy Observations
[article]
2013
arXiv
pre-print
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 perform near-optimally. We show that if the observations are noisy, perhaps surprisingly, GBS can perform very poorly. We develop EC2, a novel, greedy active
arXiv:1010.3091v2
fatcat:pa5eblqijffr5fny5p3mhzcm4a