Search Machine Learning Repository: @inproceedings{icml2014c2_hoang14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Nonmyopic $\epsilon$-Bayes-Optimal Active Learning of Gaussian Processes},
    Url = {http://jmlr.org/proceedings/papers/v32/hoang14.pdf},
    Abstract = {A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic $\epsilon$-Bayes-optimal active learning ($\epsilon$-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 $\epsilon$-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.},
    Author = {Trong N. Hoang and Bryan Low and Patrick Jaillet and Mohan Kankanhalli},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {739-747}
   }