Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes
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Jaillet_Active learning.pdf
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Author(s)
Hoang, Trong Nghia
Low, Kian Hsiang
Jaillet, Patrick
Kankanhalli, Mohan
Date Issued
2014
Journal
Machine Learning and Knowledge Discovery in Databases
Publisher
Springer-Verlag
Citation
Hoang, Trong Nghia, Kian Hsiang Low, Patrick Jaillet, and Mohan Kankanhalli. “Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes.” Lecture Notes in Computer Science (2014): 494–498.
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 [4] that jointly optimizes the trade-off. In contrast, existing works have primarily developed greedy algorithms or performed exploration and exploitation separately. To perform active learning in real time, we then propose an anytime algorithm [4] based on ε-BAL with performance guarantee and empirically demonstrate using a real-world dataset 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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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-662-44845-8_43
