Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes
Author(s)
Hoang, Trong Nghia; Low, 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 [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.
Date issued
2014Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
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
ISBN
978-3-662-44844-1
978-3-662-44845-8
ISSN
0302-9743
1611-3349