Bayesian Active Learning for Gaussian Process Classification
Neil Houlsby, Ferenc Huszar, Zoubin Ghahramani (alternate email address test) and Mate Lengyel
In: NIPS 2011: workshop on Bayesian Optimization, Experimental Design and Bandits, 16-17 Dec 2011, Granada, Spain.

Abstract

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity.

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