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#
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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.
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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.
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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.
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- 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.
Subpackages#
trieste.acquisition
trieste.models
trieste.objectives
trieste.utils