Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes
Author(s)Hoang, Trong Nghia; Low, Bryan Kian Hsiang; Jaillet, Patrick; Kankanhalli, Mohan
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A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic ϵ-Bayes-optimal active learning (ϵ-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 ϵ-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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 31st International Conference on Machine Learning
Association for Computing Machinery (ACM)
Hoang, Trong Nghia, Bryan Kian Hsiang Low, Patrick Jaillet, and Mohan Kankanhalli. "Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes." 31st International Conference on Machine Learning (2014).
Author's final manuscript