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[ASSR19]

Alexander Amini, Wilko Schwarting, Ava Soleimany, and Daniela Rus. Deep evidential regression. arXiv preprint arXiv:1910.02600, 2019.

[BGL+12]

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[BES+08]

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[BCO21]

Mickael Binois, Nicholson Collier, and Jonathan Ozik. A portfolio approach to massively parallel bayesian optimization. arXiv preprint arXiv:2110.09334, 2021.

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[BRVDW19]

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[CZZ18]

Laming Chen, Guoxin Zhang, and Eric Zhou. Fast greedy map inference for determinantal point process to improve recommendation diversity. Advances in Neural Information Processing Systems, 2018.

[CG13]

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[CGE14]

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[CDD12]

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[DBB20]

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[DKvdH+17]

Vincent Dutordoir, Nicolas Knudde, Joachim van der Herten, Ivo Couckuyt, and Tom Dhaene. Deep Gaussian process metamodeling of sequentially sampled non-stationary response surfaces. In 2017 Winter Simulation Conference (WSC), volume, 1728–1739. 2017. doi:10.1109/WSC.2017.8247911.

[EPG+19]

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[GG16]

Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: representing model uncertainty in deep learning. In International Conference on Machine Learning, 1050–1059. PMLR, 2016.

[GKZ+14]

Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, and John Cunningham. Bayesian optimization with inequality constraints. In Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research. PMLR, 22–24 Jun 2014. URL: http://proceedings.mlr.press/v32/gardner14.html.

[GT16]

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[GLRC10a]

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David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro. Kriging is well-suited to parallelize optimization. In Computational intelligence in expensive optimization problems, pages 131–162. Springer, 2010.

[GonzalezDHL16]

Javier González, Zhenwen Dai, Philipp Hennig, and Neil Lawrence. Batch bayesian optimization via local penalization. In Artificial intelligence and statistics. 2016.

[GL12]

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[HBB+19]

Ali Hebbal, Loic Brevault, Mathieu Balesdent, El-Ghazali Talbi, and Nouredine Melab. Bayesian optimization using deep Gaussian processes. arXiv preprint arXiv:1905.03350, 2019.

[HernandezLHG14]

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[KLHG21]

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[LPB16]

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[MOP23]

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[MLGR21]

Henry B Moss, David S Leslie, Javier Gonzalez, and Paul Rayson. Gibbon: general-purpose information-based bayesian optimisation. Journal of Machine Learning Research, 22:1–49, 2021.

[MLR21]

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[MLR20]

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[NR08]

Hannes Nickisch and Carl Edward Rasmussen. Approximations for binary gaussian process classification. Journal of Machine Learning Research, 9(67):2035–2078, 2008. URL: http://jmlr.org/papers/v9/nickisch08a.html.

[OA09]

Manfred Opper and Cédric Archambeau. The variational gaussian approximation revisited. Neural computation, 2009.

[OWA+21]

Ian Osband, Zheng Wen, Mohammad Asghari, Morteza Ibrahimi, Xiyuan Lu, and Benjamin Van Roy. Epistemic neural networks. arXiv preprint arXiv:2107.08924, 2021.

[PRD+17]

P. Perdikaris, M. Raissi, A. Damianou, N.D. Lawrence, and G.E Karniadakis. Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2017.

[PGR+10]

Victor Picheny, David Ginsbourger, Olivier Roustant, Raphael T Haftka, and Nam-Ho Kim. Adaptive designs of experiments for accurate approximation of target regions. Journal of Mechanical Design, 2010.

[PWG13]

Victor Picheny, Tobias Wagner, and David Ginsbourger. A benchmark of kriging-based infill criteria for noisy optimization. Structural and Multidisciplinary Optimization, 48:, 09 2013. doi:10.1007/s00158-013-0919-4.

[RBM08]

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[SEH18]

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[SKSK10]

Niranjan Srinivas, Andreas Krause, Matthias Seeger, and Sham M. Kakade. Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. In Johannes Fürnkranz and Thorsten Joachims, editors, Proceedings of the 27th International Conference on Machine Learning (ICML-10), 1015–1022. Omnipress, 2010.

[Tit09]

Michalis Titsias. Variational learning of inducing variables in sparse gaussian processes. In Artificial intelligence and statistics. 2009.

[TPD20]

Léonard Torossian, Victor Picheny, and Nicolas Durrande. Bayesian quantile and expectile optimisation. arXiv preprint arXiv:2001.04833, 2020.

[VMA+21]

Sattar Vakili, Henry Moss, Artem Artemev, Vincent Dutordoir, and Victor Picheny. Scalable thompson sampling using sparse gaussian process models. Advances in Neural Information Processing Systems, 2021.

[VVL99]

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[WJ17]

Zi Wang and Stefanie Jegelka. Max-value entropy search for efficient bayesian optimization. arXiv preprint arXiv:1703.01968, 2017.

[WZH+13]

Ziyu Wang, Masrour Zoghi, Frank Hutter, David Matheson, and Nando de Freitas. Bayesian optimization in high dimensions via random embeddings. In IJCAI, volume 13, 1778–1784. 2013.

[WBT+20]

James Wilson, Viacheslav Borovitskiy, Alexander Terenin, Peter Mostowsky, and Marc Deisenroth. Efficiently sampling functions from gaussian process posteriors. In International Conference on Machine Learning. 2020.

[WHD18]

James Wilson, Frank Hutter, and Marc Deisenroth. Maximizing acquisition functions for bayesian optimization. Advances in Neural Information Processing Systems, 2018.

[YEDBack19]

Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck. Efficient computation of expected hypervolume improvement using box decomposition algorithms. Journal of Global Optimization, 75(1):3–34, 2019.