Nonmyopic ϵ-Bayes-Optimal Active Learning of Gaussian Processes
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Jaillet_Nonmyopic.pdf
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Author(s)
Hoang, Trong Nghia
Low, Bryan Kian Hsiang
Jaillet, Patrick
Kankanhalli, Mohan
Date Issued
2014
Journal
Proceedings of the 31st International Conference on Machine Learning
Publisher
Association for Computing Machinery (ACM)
Citation
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).
Version
Author's final manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://jmlr.org/proceedings/papers/v32/
