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Nonmyopic $\epsilon$-Bayes-Optimal Active Learning of Gaussian Processes
Authors: Trong N. Hoang, Bryan Low, Patrick Jaillet and Mohan Kankanhalli
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic $\epsilon$-Bayes-optimal active learning ($\epsilon$-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily developed myopic/greedy algorithms or performed exploration and exploitation separately. To perform active learning in real time, we then propose an anytime algorithm based on $\epsilon$-BAL with performance guarantee and empirically demonstrate using synthetic and real-world datasets that, with limited budget, it outperforms the state-of-the-art algorithms.
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