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Revision b4df039c6fe478297e532720e76d1213022410d5 authored by Jesper Nielsen on 26 October 2022, 08:27:38 UTC, committed by GitHub on 26 October 2022, 08:27:38 UTC
Fix mypy error. (#2009)
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refs.bib
@article{chu2005gaussian,
  title = {Gaussian processes for ordinal regression},
  author = {Chu, Wei and Ghahramani, Zoubin},
  journal = {Journal of Machine Learning Research},
  volume = {6},
  number = {Jul},
  pages = {1019--1041},
  year = {2005}
}
@ARTICLE{GPflow2017,
    author = {Matthews, Alexander G. de G. and {van der Wilk}, Mark and Nickson, Tom and Fujii, Keisuke. and {Boukouvalas}, Alexis and {Le{\'o}n-Villagr{\'a}}, Pablo and Ghahramani, Zoubin and Hensman, James},
    title = "{{GP}flow: A {G}aussian process library using {T}ensor{F}low}",
    journal = {Journal of Machine Learning Research},
    year = {2017},
    month = {apr},
    volume = {18},
    number = {40},
    pages = {1-6},
    url = {http://jmlr.org/papers/v18/16-537.html}
}
@article{GPflow2020multioutput,
  author = {{van der Wilk}, Mark and Dutordoir, Vincent and John, ST and Artemev, Artem and Adam, Vincent and Hensman, James},
  title = {A Framework for Interdomain and Multioutput {G}aussian Processes},
  year = {2020},
  journal = {arXiv:2003.01115},
  url = {https://arxiv.org/abs/2003.01115}
}
@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{hensman2014scalable,
  title = {Scalable Variational Gaussian Process Classification},
  author = {Hensman, James and Matthews, Alexander G. de G. and Ghahramani, Zoubin},
  booktitle = {Proceedings of AISTATS},
  year = {2015}
}
@inproceedings{hensman2015mcmc,
  title = {MCMC for Variatinoally Sparse Gaussian Processes},
  author = {Hensman, James and Matthews, Alexander G. de G. and Filippone, Maurizio and Ghahramani, Zoubin},
  booktitle = {Proceedings of NIPS},
  year = {2015},
  url = {https://proceedings.neurips.cc/paper/2015/file/6b180037abbebea991d8b1232f8a8ca9-Paper.pdf},
}
@article{lawrence2003gaussian,
  title={Gaussian process latent variable models for visualisation of high dimensional data},
  author={Lawrence, Neil},
  journal={Advances in neural information processing systems},
  volume={16},
  year={2003}
}
@incollection{lloyd2014,
  author = {Lloyd, James Robert et al},
  title = {Automatic Construction and Natural-language Description of Nonparametric Regression Models},
  booktitle = {Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence},
  year = {2014},
  url = {http://dl.acm.org/citation.cfm?id=2893873.2894066},
}
@inproceedings{matthews2016sparse,
  title={On sparse variational methods and the Kullback-Leibler divergence between stochastic processes},
  author={Matthews, Alexander G de G and Hensman, James and Turner, Richard and Ghahramani, Zoubin},
  booktitle={Artificial Intelligence and Statistics},
  pages={231--239},
  year={2016},
  organization={PMLR}
}
@phdthesis{matthews2017scalable,
  title={Scalable Gaussian process inference using variational methods},
  author={Matthews, Alexander Graeme de Garis},
  year={2017},
  school={University of Cambridge}
}
@incollection{NIPS2009_3628,
  title = {Kernel Methods for Deep Learning},
  author = {Youngmin Cho and Lawrence K. Saul},
  booktitle = {Advances in Neural Information Processing Systems 22},
  year = {2009},
  url = {http://papers.nips.cc/paper/3628-kernel-methods-for-deep-learning.pdf}
}
@incollection{NIPS2009_3876,
  title = {Inter-domain Gaussian Processes for Sparse Inference using Inducing Features},
  author = {Miguel L\'{a}zaro-Gredilla and An\'{\i}bal Figueiras-Vidal},
  booktitle = {Advances in Neural Information Processing Systems 22},
  year = {2009},
}
@article{Opper:2009,
  title = {The Variational Gaussian Approximation Revisited},
  author = {Opper, Manfred and Archambeau, Cedric},
  journal = {Neural Comput.},
  year = {2009},
  pages = {786--792},
}
@InProceedings{pmlr-v139-artemev21a,
  title = {Tighter Bounds on the Log Marginal Likelihood of
  Gaussian Process Regression Using Conjugate Gradients},
  author = {Artemev, Artem and Burt, David R. and van der Wilk, Mark},
  booktitle = {Proceedings of the 38th International Conference on Machine Learning},
  pages = {362--372},
  year = {2021}
}
@book{rasmussen_williams_06,
  author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
  isbn = {026218253X},
  publisher = {MIT Press},
  series = {Adaptive computation and machine learning},
  title = {Gaussian processes for machine learning},
  year = 2006
}
@inproceedings{salimbeni18,
  title = {Natural Gradients in Practice: Non-Conjugate Variational Inference in Gaussian Process Models},
  author = {Salimbeni, Hugh and Eleftheriadis, Stefanos and Hensman, James},
  booktitle = {AISTATS},
  year = {2018}
}
@inproceedings{Snelson06sparsegaussian,
  author = {Edward Snelson and Zoubin Ghahramani},
  title = {Sparse Gaussian Processes using Pseudo-inputs},
  booktitle = {Advances In Neural Information Processing Systems},
  year = {2006},
  pages = {1257--1264},
  publisher = {MIT press}
}
@inproceedings{titsias2009variational,
  title = {Variational learning of inducing variables in sparse Gaussian processes},
  author = {Titsias, Michalis K},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  pages = {567--574},
  year = {2009}
}
@inproceedings{titsias2010bayesian,
  title={Bayesian Gaussian process latent variable model},
  author={Titsias, Michalis and Lawrence, Neil D},
  booktitle={Proceedings of the thirteenth international conference on artificial intelligence and statistics},
  pages={844--851},
  year={2010},
  organization={JMLR Workshop and Conference Proceedings}
}
@misc{titsias_2014,
  title = {Variational Inference for Gaussian and Determinantal Point Processes},
  url = {http://www2.aueb.gr/users/mtitsias/papers/titsiasNipsVar14.pdf},
  publisher = {Workshop on Advances in Variational Inference (NIPS 2014)},
  author = {Titsias, Michalis K.},
  year = {2014},
  month = {Dec}
}
@incollection{vdw2017convgp,
  title = {Convolutional Gaussian Processes},
  author = {van der Wilk, Mark and Rasmussen, Carl Edward and Hensman, James},
  booktitle = {Advances in Neural Information Processing Systems 30},
  year = {2017},
  url = {http://papers.nips.cc/paper/6877-convolutional-gaussian-processes.pdf}
}
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