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Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
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Bernhard Sch“”olkopf and Alexander Smola · 2001
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Alison Gibbs and Francis Su · 2002
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Malte Kuss and Carl Rasmussen · 2004
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“Low rank updates for the Cholesky decomposition”, 2004
Matthias Seeger · 2004
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“Gaussian Processes for Machine Learning”
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Edward Snelson and Zoubin Ghahramani · 2006
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Ali Rahimi and Benjamin Recht · 2008
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Matthias Seeger · 2008
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Jean-Paul Chiles and Pierre Delfiner · 2009
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Arnaud Doucet · 2010
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Ching-An Cheng and Byron Boots · 2017
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James Hensman, Nicolas Durrande and Arno Solin · 2017
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Jos“’e Hern“’andez-Lobato, James Requeima, Edward Pyzer-Knapp and Al“’an Aspuru-Guzik · 2017
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Anton Mallasto and Aasa Feragen · 2017
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Sanket Kamthe and Marc Deisenroth · 2018
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“Parallelised Bayesian optimisation via Thompson Sampling”
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Daniele Calandriello et al · 2019
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Ke Wang et al · 2019
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