References#

Works cited across the documentation.

[1]

Viacheslav Borovitskiy, Iskander Azangulov, Alexander Terenin, Peter Mostowsky, Marc Deisenroth, and Nicolas Durrande. Matern Gaussian processes on graphs. In International Conference on Artificial Intelligence and Statistics. 2021.

[2]

Wessel Bruinsma, Eric Perim, William Tebbutt, Scott Hosking, Arno Solin, and Richard Turner. Scalable exact inference in multi-output Gaussian processes. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, 1190–1201. PMLR, 2020. URL: https://proceedings.mlr.press/v119/bruinsma20a.html.

[3]

David Kristjanson Duvenaud. Automatic Model Construction with Gaussian Processes. PhD thesis, University of Cambridge, 2014.

[4]

Jouni Hartikainen and Simo Särkkä. Kalman filtering and smoothing solutions to temporal Gaussian process regression models. In 2010 IEEE International Workshop on Machine Learning for Signal Processing, 379–384. 2010. doi:10.1109/MLSP.2010.5589113.

[5]

James Hensman, Nicolo Fusi, and Neil D Lawrence. Gaussian processes for big data. In Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI), 282–290. 2013.

[6]

James Hensman, Alexander Matthews, and Zoubin Ghahramani. Scalable variational Gaussian process classification. In Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, volume 38 of Proceedings of Machine Learning Research, 351–360. PMLR, 2015.

[7]

Nicholas J. Higham. Accuracy and Stability of Numerical Algorithms. Society for Industrial and Applied Mathematics, Philadelphia, PA, second edition, 2002. URL: https://epubs.siam.org/doi/abs/10.1137/1.9780898718027, doi:10.1137/1.9780898718027.

[8]

Felix Leibfried, Vincent Dutordoir, ST John, and Nicolas Durrande. A tutorial on sparse Gaussian processes and variational inference. arXiv preprint arXiv:2012.13962, 2020.

[9]

Xiaoyu Lu, Alexis Boukouvalas, and James Hensman. Additive Gaussian processes revisited. In Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, 14358–14383. PMLR, 2022. URL: https://proceedings.mlr.press/v162/lu22b.html.

[10]

Anton Mallasto and Aasa Feragen. Learning from uncertain curves: the 2-Wasserstein metric for Gaussian processes. Advances in Neural Information Processing Systems, 2017.

[11]

Bertil Matérn. Spatial variation: Stochastic models and their application to some problems in forest surveys and other sampling investigations. PhD thesis, Stockholm University, 1960.

[12]

Joaquin Quiñonero-Candela and Carl Edward Rasmussen. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6(65):1939–1959, 2005.

[13]

Carl Edward Rasmussen and Christopher K Williams. Gaussian processes for machine learning. Volume 2. MIT press Cambridge, MA, 2006.

[14]

Arno Solin and Simo Särkkä. Explicit link between periodic covariance functions and state space models. In Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), volume 33 of Proceedings of Machine Learning Research, 904–912. 2014.

[15]

Simo Särkkä and Arno Solin. Applied Stochastic Differential Equations. Institute of Mathematical Statistics Textbooks. Cambridge University Press, 2019. doi:10.1017/9781108186735.

[16]

Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, volume 5 of Proceedings of Machine Learning Research, 567–574. PMLR, 2009.

[17]

Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P Xing. Deep kernel learning. In Artificial intelligence and statistics, 370–378. PMLR, 2016.

[18]

Mauricio A. Álvarez, Lorenzo Rosasco, and Neil D. Lawrence. Kernels for vector-valued functions: a review. Foundations and Trends in Machine Learning, 4(3):195–266, 2012. doi:10.1561/2200000036.