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references.bib
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% This file was created with JabRef 2.9.2.
@article{shahriari2016taking,
title={Taking the human out of the loop: A review of bayesian optimization},
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journal={Proceedings of the IEEE},
volume={104},
number={1},
pages={148--175},
year={2016},
publisher={IEEE}
}
@article{durrande2011additive,
title={Additive covariance kernels for high-dimensional Gaussian process modeling},
author={Durrande, Nicolas and Ginsbourger, David and Roustant, Olivier and Carraro, Laurent},
journal={arXiv preprint arXiv:1111.6233},
year={2011}
}
@inproceedings{cao2013efficient,
title={Efficient optimization for sparse gaussian process regression},
author={Cao, Yanshuai and Brubaker, Marcus A and Fleet, David J and Hertzmann, Aaron},
booktitle={Advances in Neural Information Processing Systems},
pages={1097--1105},
year={2013}
}
@incollection{williams1998prediction,
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booktitle={Learning in graphical models},
pages={599--621},
year={1998},
publisher={Springer}
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@article{brochu2010tutorial,
title={A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning},
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journal={arXiv preprint arXiv:1012.2599},
year={2010}
}
@article{jones1998efficient,
title={Efficient global optimization of expensive black-box functions},
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pages={455--492},
year={1998},
publisher={Springer}
}
@article{kennedy2000predicting,
title={Predicting the output from a complex computer code when fast approximations are available},
author={Kennedy, Marc C and O'Hagan, Anthony},
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volume={87},
number={1},
pages={1--13},
year={2000},
publisher={Biometrika Trust}
}
@book{forrester2008engineering,
title={Engineering design via surrogate modelling: a practical guide},
author={Forrester, Alexander and Sobester, Andras and Keane, Andy},
year={2008},
publisher={John Wiley \& Sons}
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@book{bochner1959lectures,
title={Lectures on Fourier Integrals},
author={Bochner, S. and Tenenbaum, M. and Pollard, H.},
isbn={9780691079943},
lccn={lc59005589},
series={Annals of mathematics studies},
url={https://books.google.fr/books?id=-vU02QewWK8C},
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publisher={Princeton University Press}
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@book{neal2012bayesian,
title={Bayesian learning for neural networks},
author={Neal, Radford M},
volume={118},
year={2012},
publisher={Springer Science \& Business Media}
}
@book{matheron1962traité,
title={Trait{\'e} de g{\'e}ostatistique appliqu{\'e}e},
author={Matheron, G.},
number={v. 1},
lccn={62044574},
series={Memoires},
url={https://books.google.fr/books?id=88YKAQAAMAAJ},
year={1962},
publisher={{\'E}ditions Technip}
}
@article{ng2014hierarchical,
title={Hierarchical Mixture-of-Experts Model for Large-Scale Gaussian Process Regression},
author={Ng, Jun Wei and Deisenroth, Marc Peter},
journal={arXiv preprint arXiv:1412.3078},
year={2014}
}
@article{deisenroth2015distributed,
title={Distributed Gaussian Processes},
author={Deisenroth, Marc Peter and Ng, Jun Wei},
journal={arXiv preprint arXiv:1502.02843},
year={2015}
}
@article{chen2009bagging,
title={Bagging for Gaussian process regression},
author={Chen, Tao and Ren, Jianghong},
journal={Neurocomputing},
volume={72},
number={7},
pages={1605--1610},
year={2009},
publisher={Elsevier}
}
@article{rasmussen2002infinite,
title={Infinite mixtures of Gaussian process experts},
author={Rasmussen, Carl Edward and Ghahramani, Zoubin},
journal={Advances in neural information processing systems},
volume={2},
pages={881--888},
year={2002},
publisher={MIT; 1998}
}
@article{Gal2014Distributed,
Author = {Yarin Gal and Mark van der Wilk and Carl E. Rasmussen},
Title = {Distributed Variational Inference in Sparse {G}aussian Process Regression and Latent Variable Models},
Year = {2014},
Journal = {arXiv:1402.1389},
}
@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{seeger2003fast,
title={Fast forward selection to speed up sparse Gaussian process regression},
author={Seeger, Matthias and Williams, Christopher and Lawrence, Neil},
booktitle={Artificial Intelligence and Statistics 9},
year={2003}
}
@article{wilson2015thoughts,
title={Thoughts on Massively Scalable Gaussian Processes},
author={Wilson, Andrew Gordon and Dann, Christoph and Nickisch, Hannes},
journal={arXiv preprint arXiv:1511.01870},
year={2015}
}
@article{wilson2013gpatt,
title={GPatt: Fast Multidimensional Pattern Extrapolation with Gaussian Processes},
author={Wilson, Andrew Gordon and Gilboa, Elad and Nehorai, Arye and Cunningham, John P},
journal={arXiv preprint arXiv:1310.5288},
year={2013}
}
@article{alvarez2011kernels,
title={Kernels for vector-valued functions: A review},
author={Alvarez, Mauricio A and Rosasco, Lorenzo and Lawrence, Neil D},
journal={arXiv preprint arXiv:1106.6251},
year={2011}
}
@inproceedings{bonilla2007multi,
title={Multi-task Gaussian process prediction},
author={Bonilla, Edwin V and Chai, Kian M and Williams, Christopher},
booktitle={Advances in neural information processing systems},
pages={153--160},
year={2007}
}
@book{goovaerts1997geostatistics,
title={Geostatistics for Natural Resources Evaluation},
author={Goovaerts, P.},
isbn={9780195115383},
lccn={97002521},
series={Applied geostatistics series},
url={https://books.google.fr/books?id=CW-7tHAaVR0C},
year={1997},
publisher={Oxford University Press}
}
@book{timoshenko1953history,
title={History of Strength of Materials: With a Brief Account of the History of Theory of Elasticity and Theory of Structures},
author={Timoshenko, S.},
isbn={9780486611877},
lccn={82017713},
series={Dover Civil and Mechanical Engineering Series},
url={https://books.google.fr/books?id=tkScQmyhsb8C},
year={1953},
publisher={Dover Publications}
}
@inproceedings{le2013fastfood,
title={Fastfood-computing hilbert space expansions in loglinear time},
author={Le, Quoc and Sarl{\'o}s, Tam{\'a}s and Smola, Alexander},
booktitle={Proceedings of the 30th International Conference on Machine Learning},
pages={244--252},
year={2013}
}
@INPROCEEDINGS{Moore99,
AUTHOR = "R. Moore and J. Lopes",
TITLE = "Paper templates",
BOOKTITLE = "TEMPLATE'06, 1st International Conference on Template Production",
YEAR = "1999",
publisher = "SCITEPRESS",
file = F
}
@BOOK{Smith98,
AUTHOR = "J. Smith",
TITLE = "The Book",
PUBLISHER = "The publishing company",
YEAR = "1998",
address = "London",
edition = "2nd",
file = F
}
@article {Constantinescu2013,
title = {Physics-Based Covariance Models for Gaussian Processes with Multiple Outputs},
journal = {International Journal for Uncertainty Quantification},
volume = {3},
year = {2013},
chapter = {47-71},
author = {Emil M. Constantinescu and Mihai Anitescu}
}
@INPROCEEDINGS{Titsias09variationallearning,
author = {Michalis K. Titsias},
title = {Variational learning of inducing variables in sparse Gaussian processes},
booktitle = {In Artificial Intelligence and Statistics 12},
year = {2009},
pages = {567--574}
}
@book {Stein1999Springer,
title = {{Interpolation of Spatial Data: Some Theory for Kriging}},
year = {1999},
pages = {247},
publisher = {Springer},
organization = {Springer},
address = {New York},
author = {Stein, M.~L.}
}
@INPROCEEDINGS{Boyle05dependentgaussian,
author = {Phillip Boyle and Marcus Frean},
title = {Dependent Gaussian processes},
booktitle = {In Advances in Neural Information Processing Systems 17},
year = {2005},
pages = {217--224},
publisher = {MIT Press}
}
@article{caoF14,
author = {Yanshuai Cao and
David J. Fleet},
title = {Generalized Product of Experts for Automatic and Principled Fusion
of Gaussian Process Predictions},
journal = {CoRR},
volume = {abs/1410.7827},
year = {2014},
url = {http://arxiv.org/abs/1410.7827},
timestamp = {Sun, 02 Nov 2014 11:25:59 +0100},
biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/CaoF14},
bibsource = {dblp computer science bibliography, http://dblp.org}
}
@article{sandboge2010fluid,
title={Fluid--structure interaction with OpenFSITM and MD NastranTM structural solver},
author={Sandboge, Robert},
journal={Ann. Arbor},
volume={1001},
year={2010}
}
@article{cambier2008status,
title={Status of the elsA CFD software for flow simulation and multidisciplinary applications},
author={Cambier, L and Veuillot, JP},
journal={AIAA paper},
volume={664},
pages={2008},
year={2008}
}
@incollection{bonilla_multi-task_2008,
added-at = {2009-03-05T08:48:22.000+0100},
address = {Cambridge, MA},
author = {Bonilla, Edwin and Chai, Kian Ming and Williams, Chris},
biburl = {http://www.bibsonomy.org/bibtex/2973351cea44d5803e83e89081cb4f706/bcao},
booktitle = {Advances in Neural Information Processing Systems 20},
editor = {Platt, J.C. and Koller, D. and Singer, Y. and Roweis, S.},
interhash = {175901df736df5fa57337f2e9008362c},
intrahash = {973351cea44d5803e83e89081cb4f706},
keywords = {},
pages = {153--160},
publisher = {MIT Press},
timestamp = {2009-03-05T08:48:22.000+0100},
title = {Multi-task Gaussian Process Prediction},
year = 2008
}
@book{mathworks2005matlab,
title={MATLAB: the language of technical computing. Desktop tools and development environment, version 7},
author={MathWorks, Inc},
volume={9},
year={2005},
publisher={MathWorks}
}
@article{march2015askit,
title={ASKIT: Approximate skeletonization kernel-independent treecode in high dimensions},
author={March, William B and Xiao, Bo and Biros, George},
journal={SIAM Journal on Scientific Computing},
volume={37},
number={2},
pages={A1089--A1110},
year={2015},
publisher={SIAM}
}
@article{belta2002svd,
title={An SVD-based projection method for interpolation on SE (3)},
author={Belta, Calin and Kumar, Vijay},
journal={IEEE transactions on Robotics and Automation},
volume={18},
number={3},
pages={334--345},
year={2002},
publisher={IEEE}
}
@book{Rasmussen2005,
author = {Rasmussen, Carl Edward and Williams, Christopher K. I.},
title = {Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)},
year = {2005},
isbn = {026218253X},
publisher = {The MIT Press},
}
@inproceedings{journals/jmlr/AlvarezLL09,
added-at = {2013-11-25T00:00:00.000+0100},
author = {Alvarez, Mauricio A. and Luengo, David and Lawrence, Neil D.},
biburl = {http://www.bibsonomy.org/bibtex/23c762536cede7b58f85ec82a4f77f7b8/dblp},
booktitle = {AISTATS},
editor = {Dyk, David A. Van and Welling, Max},
ee = {http://www.jmlr.org/proceedings/papers/v5/alvarez09a.html},
interhash = {68d9e013ed8443994633bccbd782de46},
intrahash = {3c762536cede7b58f85ec82a4f77f7b8},
keywords = {dblp},
pages = {9-16},
publisher = {JMLR.org},
series = {JMLR Proceedings},
timestamp = {2015-06-18T19:51:38.000+0200},
title = {Latent Force Models.},
url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlrp5.html#AlvarezLL09},
volume = 5,
year = 2009
}
@incollection{NIPSDerivativeGP,
title = {Derivative Observations in Gaussian Process Models of Dynamic Systems},
author = {E. Solak and R. Murray-smith and W. E. Leithead and D. J. Leith and Carl E. Rasmussen},
booktitle = {Advances in Neural Information Processing Systems 15},
editor = {S. Becker and S. Thrun and K. Obermayer},
pages = {1057--1064},
year = {2003},
publisher = {MIT Press},
url = {http://papers.nips.cc/paper/2287-derivative-observations-in-gaussian-process-models-of-dynamic-systems.pdf}
}
@incollection{SparseMOGPAlvarez2008,
title = {Sparse Convolved Gaussian Processes for Multi-output Regression},
author = {Mauricio Alvarez and Neil D. Lawrence},
booktitle = {Advances in Neural Information Processing Systems 21},
editor = {D. Koller and D. Schuurmans and Y. Bengio and L. Bottou},
pages = {57--64},
year = {2009},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/3553-sparse-convolved-gaussian-processes-for-multi-output-regression.pdf}
}
@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}
}
@ARTICLE{Quiñonero-candela05aunifying,
author = {Joaquin Quiñonero-candela and Carl Edward Rasmussen and Ralf Herbrich},
title = {A unifying view of sparse approximate Gaussian process regression},
journal = {Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {2005}
}
@inproceedings{journals/jmlr/AlvarezLTL10,
added-at = {2013-11-25T00:00:00.000+0100},
author = {\'Alvarez, Mauricio A. and Luengo, David and Titsias, Michalis K. and Lawrence, Neil D.},
biburl = {http://www.bibsonomy.org/bibtex/24dcf9c7239667c65284cd307127f715c/dblp},
booktitle = {AISTATS},
crossref = {conf/aistats/2010},
editor = {Teh, Yee Whye and Titterington, D. Mike},
ee = {http://www.jmlr.org/proceedings/papers/v9/alvarez10a.html},
interhash = {98009002b7c6a7219a343ffdbdd2ddeb},
intrahash = {4dcf9c7239667c65284cd307127f715c},
keywords = {dblp},
pages = {25-32},
publisher = {JMLR.org},
series = {JMLR Proceedings},
timestamp = {2015-06-18T19:53:55.000+0200},
title = {Efficient Multioutput Gaussian Processes through Variational Inducing Kernels.},
url = {http://dblp.uni-trier.de/db/journals/jmlr/jmlrp9.html#AlvarezLTL10},
volume = 9,
year = 2010
}
@article{Alvarez:2011:CEC:1953048.2021048,
author = {\'{A}lvarez, Mauricio A. and Lawrence, Neil D.},
title = {Computationally Efficient Convolved Multiple Output Gaussian Processes},
journal = {J. Mach. Learn. Res.},
issue_date = {2/1/2011},
volume = {12},
month = jul,
year = {2011},
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