Active Learning of Linear Embeddings for Gaussian Processes

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• Roman Garnett, University of Bonn
• Tuesday 10 June 2014, 11:00-12:00
• Engineering Department, CBL Room BE-438..

If you have a question about this talk, please contact David Duvenaud.

We propose an active learning method for discovering low-dimensional structure in high-dimensional Gaussian process (GP) tasks. Such problems are increasingly frequent and important, but have hitherto presented severe practical difficulties. We further introduce a novel technique for approximately marginalizing GP hyperparameters, yielding marginal predictions robust to hyperparameter mis-specification. Our method offers an efficient means of performing GP regression, quadrature, or Bayesian optimization in high-dimensional spaces.

This talk is part of the Machine Learning @ CUED series.

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