Active Learning of Linear Embeddings for Gaussian Processes

Roman Garnett; Michael A. Osborne; Philipp Hennig

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arXiv · 1310.6740

Active Learning of Linear Embeddings for Gaussian Processes

Roman Garnett·Michael A. Osborne·Philipp Hennig

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Abstract

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.

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Roman Garnett, Michael A. Osborne, Philipp Hennig. 2013-10-24. Active Learning of Linear Embeddings for Gaussian Processes. https://arxiv.org/abs/1310.6740

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