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This tutorial aims to provide an intuitive introduction to Gaussian process regression (GPR).
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C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning . Springer, 2006
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Z. Ghahramani, “A Tutorial on Gaussian Processes (or why I don’t use SVMs),” in Machine Learning Summer School (MLSS) , 2011
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K. P. Murphy, Machine Learning: A Probabilistic Perspective . The MIT Press, 2012
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R. Frigola, F. Lindsten, T. B. Schön, and C. E. Rasmussen, “Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC,” in Advances in Neural Information Processing Systems , 2013, pp. 3156–3164
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Cited alongside, same era.
D. Duvenaud, “Automatic model construction with Gaussian processes,” Ph.D. dissertation, University of Cambridge, 2014
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Cited alongside, same era.
D. Duvenaud, “The Kernel Cookbook,” Available at https://www.cs.toronto.edu/~duvenaud/cookbook , 2016
2016
Cited alongside, same era.
A. G. De G. Matthews, M. Van Der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahramani, and J. Hensman, “GPflow: A Gaussian process library using TensorFlow,” The Journal of Machine Learning Research , vol. 18, no. 1, pp. 1299–1304, 2017
2017
Cited alongside, same era.
Z. Chen and B. Wang, “How priors of initial hyperparameters affect Gaussian process regression models,” Neurocomputing , vol. 275, pp. 1702–1710, 2018
2018
Later among the works it cites.
J. R. Gardner, G. Pleiss, D. Bindel, K. Q. Weinberger, and A. G. Wilson, “GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration,” in Advances in Neural Information Processing Systems , 2018
2018
Later among the works it cites.
Z. Dai, “Computationally efficient GPs,” Available at https://www.youtube.com/watch?v=7mCfkIuNHYw , 2019
2019
Later among the works it cites.
H. Liu, Y.-S. Ong, X. Shen, and J. Cai, “When Gaussian process meets big data: A review of scalable GPs,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Closest in time.
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