Bibliography#

[ABvdW21]

Artem Artemev, David R. Burt, and Mark van der Wilk. Tighter bounds on the log marginal likelihood of gaussian process regression using conjugate gradients. In Proceedings of the 38th International Conference on Machine Learning, 362–372. 2021.

[CS09]

Youngmin Cho and Lawrence K. Saul. Kernel methods for deep learning. In Advances in Neural Information Processing Systems 22. 2009. URL: http://papers.nips.cc/paper/3628-kernel-methods-for-deep-learning.pdf.

[CG05]

Wei Chu and Zoubin Ghahramani. Gaussian processes for ordinal regression. Journal of Machine Learning Research, 6(Jul):1019–1041, 2005.

[HFL13]

James Hensman, Nicolo Fusi, and Neil D Lawrence. Gaussian processes for big data. arXiv preprint arXiv:1309.6835, 2013.

[HMFG15]

James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, and Zoubin Ghahramani. Mcmc for variatinoally sparse gaussian processes. In Proceedings of NIPS. 2015. URL: https://proceedings.neurips.cc/paper/2015/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf.

[HMG15]

James Hensman, Alexander G. de G. Matthews, and Zoubin Ghahramani. Scalable variational gaussian process classification. In Proceedings of AISTATS. 2015.

[LazaroGFV09]

Miguel Lázaro-Gredilla and An\'ıbal Figueiras-Vidal. Inter-domain gaussian processes for sparse inference using inducing features. In Advances in Neural Information Processing Systems 22. 2009.

[Law03]

Neil Lawrence. Gaussian process latent variable models for visualisation of high dimensional data. Advances in neural information processing systems, 2003.

[Llo14]

James Robert et al Lloyd. Automatic construction and natural-language description of nonparametric regression models. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. 2014. URL: http://dl.acm.org/citation.cfm?id=2893873.2894066.

[MHTG16]

Alexander G de G Matthews, James Hensman, Richard Turner, and Zoubin Ghahramani. On sparse variational methods and the kullback-leibler divergence between stochastic processes. In Artificial Intelligence and Statistics, 231–239. PMLR, 2016.

[MvandWilkN+17]

Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke. Fujii, Alexis Boukouvalas, Pablo León-Villagrá, Zoubin Ghahramani, and James Hensman. GPflow: A Gaussian process library using TensorFlow. Journal of Machine Learning Research, 18(40):1–6, apr 2017. URL: http://jmlr.org/papers/v18/16-537.html.

[Mat17]

Alexander Graeme de Garis Matthews. Scalable Gaussian process inference using variational methods. PhD thesis, University of Cambridge, 2017.

[OA09]

Manfred Opper and Cedric Archambeau. The variational gaussian approximation revisited. Neural Comput., pages 786–792, 2009.

[SEH18]

Hugh Salimbeni, Stefanos Eleftheriadis, and James Hensman. Natural gradients in practice: non-conjugate variational inference in gaussian process models. In AISTATS. 2018.

[SG06]

Edward Snelson and Zoubin Ghahramani. Sparse gaussian processes using pseudo-inputs. In Advances In Neural Information Processing Systems, 1257–1264. MIT press, 2006.

[TL10]

Michalis Titsias and Neil D Lawrence. Bayesian gaussian process latent variable model. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, 844–851. JMLR Workshop and Conference Proceedings, 2010.

[Tit09]

Michalis K Titsias. Variational learning of inducing variables in sparse gaussian processes. In International Conference on Artificial Intelligence and Statistics, 567–574. 2009.

[Tit14]

Michalis K. Titsias. Variational inference for gaussian and determinantal point processes. Dec 2014. URL: http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf.

[vdWRH17]

Mark van der Wilk, Carl Edward Rasmussen, and James Hensman. Convolutional gaussian processes. In Advances in Neural Information Processing Systems 30. 2017. URL: http://papers.nips.cc/paper/6877-convolutional-gaussian-processes.pdf.

[vandWilkDJ+20]

Mark van der Wilk, Vincent Dutordoir, ST John, Artem Artemev, Vincent Adam, and James Hensman. A framework for interdomain and multioutput Gaussian processes. arXiv:2003.01115, 2020. URL: https://arxiv.org/abs/2003.01115.