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SUMMARY:Active Learning of Linear Embeddings for Gaussian Processes - Roma
 n Garnett\, University of Bonn
DTSTART:20140610T100000Z
DTEND:20140610T110000Z
UID:TALK52890@talks.cam.ac.uk
CONTACT:David Duvenaud
DESCRIPTION:We propose an active learning method for discovering low-dimen
 sional structure in high-dimensional Gaussian process (GP) tasks. Such pro
 blems are increasingly frequent and important\, but have hitherto presente
 d severe practical difficulties. We further introduce a novel technique fo
 r approximately marginalizing GP hyperparameters\, yielding marginal predi
 ctions robust to hyperparameter mis-specification. Our method offers an ef
 ficient means of performing GP regression\, quadrature\, or Bayesian optim
 ization in high-dimensional spaces.\n
LOCATION:Engineering Department\, CBL Room BE-438.
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