Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes
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.
Cite
Text
Hoang et al. "Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes." International Conference on Machine Learning, 2014.Markdown
[Hoang et al. "Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes." International Conference on Machine Learning, 2014.](https://mlanthology.org/icml/2014/hoang2014icml-nonmyopic/)BibTeX
@inproceedings{hoang2014icml-nonmyopic,
title = {{Nonmyopic Ε-Bayes-Optimal Active Learning of Gaussian Processes}},
author = {Hoang, Trong Nghia and Low, Bryan Kian Hsiang and Jaillet, Patrick and Kankanhalli, Mohan},
booktitle = {International Conference on Machine Learning},
year = {2014},
pages = {739-747},
volume = {32},
url = {https://mlanthology.org/icml/2014/hoang2014icml-nonmyopic/}
}