Bayesian Active Model Selection with an Application to Automated Audiometry
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.
# SummaryThe authors introduce a new method for actively selecting the model that best fits a dataset. Contrary to active learning, where the next learning point is chosen to get a better estimate of the model hyperparameters, this methods selects the next point to better distinguish between a set of models. Similar active model selection techniques exist, but they need to retrain each model for each new data point to evaluate. The strength of the author's method is that is only requires to evaluate the predictive distributions of models, without retraining.They propose to apply this method to detect noise-induced hearing loss. The traditional way of screening for NIHL involves testing a wide range of intensities and frequencies, which is time consuming. The authors show that with their method, the number of tests to be run could be drastically decreased, reducing the cost of large-scale screenings for NIHL.# QualityThe paper is technically sound. Although not detailed, the derivations make sense. However, there is no theoretical justification or discussion, and the empirical evaluation is weak. The authors only compare their active model selection scheme with a "traditional" active learning algorithm, and a random baseline, on simulated data generated from their model (possibly biasing the results). Ideally, it should also be evaluated on real data with true examples of NIHL (not generated from the model), and also compared with other active model selection methods. Moreover, some key issues, such as defining the set of candidate points or candidate models, are not discussed at all.# ClarityThe paper is very well written. The structure is clear, and the relevance and usefulness of the proposed method are well introduced. The application is particularly well explained. A pleasure to read!# OriginalityAs stated by the authors, the problem of active model selection is less well-studied than that of active learning. The proposed method is a variation of exiting ones (maximizing mutual information instead of expected cross entropy, for example), that relies on various existing approximations for its implementation. Its application is also original, and not well studied.# SignificanceThe method gives a significant complexity improvement over existing ones, as it does not require retraining the models for each new candidate. It enables real-time application of the method, which is key to some problems. It also allows to reduce significantly the number of testing points required for NIHL screening, which would have a significant impact on large-scale screening, reducing costs.# Pros* Well written and clear * New method for active model selection, that does not require retraining models * Significant gains (fewer samples required) for an important and practical application# Cons* No theoretical analysis/discussion of the new method * Weak empirical evaluation * No comparison with other active model selection techniques * Evaluation on data simulated from their own model, thus possibly biasing results * No discussion of the problem of generating the set of candidate points or models