references.bib

@inproceedings{borovitskiy2021matern,
  title     = {Matern {G}aussian processes on graphs},
  author    = {Borovitskiy, Viacheslav and Azangulov, Iskander and Terenin, Alexander and Mostowsky, Peter and Deisenroth, Marc and Durrande, Nicolas},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2021}
}

@article{quinonero-candela2005gaussian,
  author  = {Joaquin Qui{{\~n}}onero-Candela and Carl Edward Rasmussen},
  title   = {A Unifying View of Sparse Approximate {G}aussian Process Regression},
  journal = {Journal of Machine Learning Research},
  year    = {2005},
  volume  = {6},
  number  = {65},
  pages   = {1939-1959}
}

@book{rasmussen2006gaussian,
  title     = {Gaussian processes for machine learning},
  author    = {Rasmussen, Carl Edward and Williams, Christopher K},
  volume    = {2},
  number    = {3},
  year      = {2006},
  publisher = {MIT press Cambridge, MA}
}

@article{hensman2013gaussian,
  title   = {Gaussian processes for big data},
  author  = {Hensman, James and Fusi, Nicolo and Lawrence, Neil D},
  journal = {arXiv preprint arXiv:1309.6835},
  year    = {2013}
}


@inproceedings{hensman2015gaussian,
  title     = {Scalable Variational {G}aussian Process Classification},
  author    = {Hensman, James and Matthews, Alexander and Ghahramani, Zoubin},
  booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
  pages     = {351--360},
  year      = {2015},
  volume    = {38},
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR}
}


@article{leibfried2020tutorial,
  title   = {A tutorial on sparse {G}aussian processes and variational inference},
  author  = {Leibfried, Felix and Dutordoir, Vincent and John, ST and Durrande, Nicolas},
  journal = {arXiv preprint arXiv:2012.13962},
  year    = {2020}
}

@article{mallasto2017learning,
  title   = {Learning from uncertain curves: The 2-{W}asserstein metric for {G}aussian processes},
  author  = {Mallasto, Anton and Feragen, Aasa},
  journal = {Advances in Neural Information Processing Systems},
  volume  = {30},
  year    = {2017}
}

@inproceedings{wilson2016deep,
  title        = {Deep kernel learning},
  author       = {Wilson, Andrew Gordon and Hu, Zhiting and Salakhutdinov, Ruslan and Xing, Eric P},
  booktitle    = {Artificial intelligence and statistics},
  pages        = {370--378},
  year         = {2016},
  organization = {PMLR}
}


@inproceedings{titsias2009,
  title     = {Variational Learning of Inducing Variables in Sparse {G}aussian Processes},
  author    = {Titsias, Michalis},
  booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics},
  pages     = {567--574},
  year      = {2009},
  volume    = {5},
  series    = {Proceedings of Machine Learning Research},
  publisher = {PMLR}
}

@misc{salimbeni2018,
  author    = {Hugh Salimbeni and
               Stefanos Eleftheriadis and
               James Hensman},
  title     = {Natural Gradients in Practice: Non-Conjugate Variational Inference
               in {G}aussian Process Models},
  booktitle = {International Conference on Artificial Intelligence and Statistics,
               {AISTATS}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {84},
  pages     = {689--697},
  publisher = {{PMLR}},
  year      = {2018}
}

@article{lao2020tfp,
  title   = {tfp. mcmc: Modern Markov chain Monte Carlo tools built for modern hardware},
  author  = {Lao, Junpeng and Suter, Christopher and Langmore, Ian and Chimisov, Cyril and Saxena, Ashish and Sountsov, Pavel and Moore, Dave and Saurous, Rif A and Hoffman, Matthew D and Dillon, Joshua V},
  journal = {arXiv preprint arXiv:2002.01184},
  year    = {2020}
}