Bayesian active model selection with an application to automated audiometry

Jacob R. Gardner, Kilian Q. Weinberger, Gustavo Malkomes, Dennis Barbour, Roman Garnett, John P. Cunningham

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


We introduce a novel information-theoretic approach for active model selection and demonstrate its effectiveness in a real-world application. Although our method can work with arbitrary models, we focus on actively learning the appropriate structure for Gaussian process (GP) models with arbitrary observation likelihoods. We then apply this framework to rapid screening for noise-induced hearing loss (NIHL), a widespread and preventible disability, if diagnosed early. We construct a GP model for pure-tone audiometric responses of patients with NIHL. Using this and a previously published model for healthy responses, the proposed method is shown to be capable of diagnosing the presence or absence of NIHL with drastically fewer samples than existing approaches. Further, the method is extremely fast and enables the diagnosis to be performed in real time.

Original languageEnglish
Pages (from-to)2386-2394
Number of pages9
JournalAdvances in Neural Information Processing Systems
StatePublished - 2015
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015


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