https://github.com/GPflow/GPflow
Tip revision: 8fa6df5c5abf87461fd5a3d03589956816df27b2 authored by Artem Artemev on 12 March 2023, 18:01:31 UTC
Merge branch 'develop' into awav/sparse-tensors-support
Merge branch 'develop' into awav/sparse-tensors-support
Tip revision: 8fa6df5
refs.bib
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title = {Gaussian processes for ordinal regression},
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journal = {Journal of Machine Learning Research},
volume = {6},
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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,
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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}
}