Uncertainty in Artificial Intelligence
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Active Learning of Linear Embeddings for Gaussian Processes
Roman Garnett, Michael Osborne, Philipp Hennig
We propose an active learning method for discovering low-dimensional structure in high- dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and impor- tant, but have hitherto presented severe practical difficulties. We further introduce a novel tech- nique for approximately marginalizing GP hyper- parameters, yielding marginal predictions robust to hyperparameter misspecification. Our method offers an efficient means of performing GP re- gression, quadrature, or Bayesian optimization in high-dimensional spaces.
Pages: 230-239
PS Link:
PDF Link: /papers/14/p230-garnett.pdf
AUTHOR = "Roman Garnett and Michael Osborne and Philipp Hennig",
TITLE = "Active Learning of Linear Embeddings for Gaussian Processes",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "230--239"

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