trieste#

The library root. See bayesian_optimizer for the core optimizer, which requires models (see models), and data sets (see data). The acquisition package provides a selection of acquisition algorithms and the functionality to define your own. The ask_tell_optimization package provides API for Ask-Tell optimization and manual control of the optimization loop. The objectives package contains several popular objective functions, useful for experimentation.

Bibliography#

ARC+19

Ahsan Alvi, Binxin Ru, Jan-Peter Calliess, Stephen Roberts, and Michael A Osborne. Asynchronous batch bayesian optimisation with improved local penalisation. In International Conference on Machine Learning. 2019.

ASSR19

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

BGL+12

Julien Bect, David Ginsbourger, Ling Li, Victor Picheny, and Emmanuel Vazquez. Sequential design of computer experiments for the estimation of a probability of failure. Statistics and Computing, 22(3):773–793, 2012.

BES+08

Barron J Bichon, Michael S Eldred, Laura Painton Swiler, Sandaran Mahadevan, and John M McFarland. Efficient global reliability analysis for nonlinear implicit performance functions. AIAA journal, 46(10):2459–2468, 2008.

BCKW15

Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In International Conference on Machine Learning, 1613–1622. PMLR, 2015.

CGE14

Clément Chevalier, David Ginsbourger, and Xavier Emery. Corrected kriging update formulae for batch-sequential data assimilation. In Mathematics of Planet Earth, pages 119–122. Springer, 2014.

CDD12

Ivo Couckuyt, Dirk Deschrijver, and Tom Dhaene. Towards efficient multiobjective optimization: multiobjective statistical criterions. 2012 IEEE Congress on Evolutionary Computation, CEC 2012, pages 10–15, 2012. doi:10.1109/CEC.2012.6256586.

DBB20

Samuel Daulton, Maximilian Balandat, and Eytan Bakshy. Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization. arXiv preprint arXiv:2006.05078, 2020.

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

David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek. Scalable global optimization via local Bayesian optimization. In Advances in Neural Information Processing Systems, 5496–5507. 2019. URL: http://papers.nips.cc/paper/8788-scalable-global-optimization-via-local-bayesian-optimization.pdf.

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.

GLRC10a

David Ginsbourger, Rodolphe Le Riche, and Laurent Carraro. Kriging Is Well-Suited to Parallelize Optimization, pages 131–162. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010. URL: https://doi.org/10.1007/978-3-642-10701-6_6, doi:10.1007/978-3-642-10701-6_6.

GLRC10b

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

Robert B Gramacy and Herbert KH Lee. Cases for the nugget in modeling computer experiments. Statistics and Computing, 22(3):713–722, 2012.

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

JM Hernández-Lobato, MW Hoffman, and Z Ghahramani. Predictive entropy search for efficient global optimization of black-box functions. Advances in Neural Information Processing Systems, 2014.

HernandezLA15

José Miguel Hernández-Lobato and Ryan Adams. Probabilistic backpropagation for scalable learning of bayesian neural networks. In International Conference on Machine Learning, 1861–1869. PMLR, 2015.

HernandezLRPKAG17

José Miguel Hernández-Lobato, James Requeima, Edward O Pyzer-Knapp, and Alán Aspuru-Guzik. Parallel and distributed thompson sampling for large-scale accelerated exploration of chemical space. In International conference on machine learning. 2017.

HHGL11

Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. Bayesian active learning for classification and preference learning. 2011. arXiv:1112.5745.

HANZ06

Deng Huang, Theodore T Allen, William I Notz, and Ning Zeng. Global optimization of stochastic black-box systems via sequential kriging meta-models. Journal of global optimization, 2006.

JSW98

Donald R Jones, Matthias Schonlau, and William J Welch. Efficient global optimization of expensive black-box functions. Journal of Global optimization, 13(4):455–492, 1998.

KLHG21

Arlind Kadra, Marius Lindauer, Frank Hutter, and Josif Grabocka. Well-tuned simple nets excel on tabular datasets. Advances in Neural Information Processing Systems, 2021.

KKSP18

Kirthevasan Kandasamy, Akshay Krishnamurthy, Jeff Schneider, and Barnabas Poczos. Parallelised bayesian optimisation via thompson sampling. In Amos Storkey and Fernando Perez-Cruz, editors, Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, volume 84 of Proceedings of Machine Learning Research, 133–142. PMLR, 2018. URL: https://proceedings.mlr.press/v84/kandasamy18a.html.

LPB16

Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv preprint arXiv:1612.01474, 2016.

Mac92

David J. C. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4):590–604, 1992.

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.

MLR20

Henry B. Moss, David S. Leslie, and Paul Rayson. Bosh: bayesian optimization by sampling hierarchically. ArXiv, 2020.

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.

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

Pritam Ranjan, Derek Bingham, and George Michailidis. Sequential experiment design for contour estimation from complex computer codes. Technometrics, 50(4):527–541, 2008.

SEH18

Hugh Salimbeni, Stefanos Eleftheriadis, and James Hensman. Natural gradients in practice: non-conjugate variational inference in gaussian process models. International Conference on Artificial Intelligence and Statistics, 2018.

SWJ98

Matthias Schonlau, William J Welch, and Donald R Jones. Global versus local search in constrained optimization of computer models. Lecture Notes-Monograph Series, pages 11–25, 1998.

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

David A Van Veldhuizen and Gary B Lamont. Multiobjective evolutionary algorithm test suites. In Proceedings of the 1999 ACM symposium on Applied computing, 351–357. 1999.

WJ17

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

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.