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Bayesian Active Model Selection with an Application to Automated Audiometry
Jacob Gardner · Gustavo Malkomes · Roman Garnett · Kilian Weinberger · Dennis Barbour · John Cunningham

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #19

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.

Author Information

Jacob Gardner (Cornell University)
Gustavo Malkomes (Washington University in St. Louis)
Roman Garnett (Washington University in STL)
Kilian Weinberger (Cornell University)
Dennis Barbour (Washington University in St. Louis)
John Cunningham (University of Columbia)

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