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Ultrahigh Specific Strength by Bayesian Optimization of Carbon Nanolattices. [PDF]
Adv MaterSerles P +19 more
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Bayesian optimization of cortical neuroprosthetic vision using perceptual feedback
Küçükoğlu B +7 more
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Proceedings of the Companion Conference on Genetic and Evolutionary Computation, 2023
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way.
Ivo Couckuyt +3 more
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Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way.
Ivo Couckuyt +3 more
+5 more sources
Bayesian Distributionally Robust Optimization
SIAM Journal on Optimization, 2023 We introduce a new framework, Bayesian Distributionally Robust Optimization (Bayesian-DRO), for data-driven stochastic optimization where the underlying distribution is unknown. Bayesian-DRO contrasts with most of the existing DRO approaches in the use of Bayesian estimation of the unknown distribution.
Alexander Shapiro +2 more
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International Journal of Data Science and Analytics, 2017
Tuning hyperparameters of machine learning models is important for their performance. Bayesian optimization has recently emerged as a de-facto method for this task. The hyperparameter tuning is usually performed by looking at model performance on a validation set.
Thanh Dai Nguyen +3 more
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Tuning hyperparameters of machine learning models is important for their performance. Bayesian optimization has recently emerged as a de-facto method for this task. The hyperparameter tuning is usually performed by looking at model performance on a validation set.
Thanh Dai Nguyen +3 more
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2016
Multi-stage cascade processes are fairly common, especially in manufacturing industry. Precursors or raw materials are transformed at each stage before being used as the input to the next stage. Setting the right control parameters at each stage is important to achieve high quality products at low cost.
Thanh Dai Nguyen +6 more
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Multi-stage cascade processes are fairly common, especially in manufacturing industry. Precursors or raw materials are transformed at each stage before being used as the input to the next stage. Setting the right control parameters at each stage is important to achieve high quality products at low cost.
Thanh Dai Nguyen +6 more
openaire +1 more source
Bayesian optimization and genericity
Operations Research Letters, 1997zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2022
Ivo Couckuyt +2 more
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Ivo Couckuyt +2 more
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Bayesian optimal knapsack procurement
European Journal of Operational Research, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ludwig Ensthaler, Thomas Giebe
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Bayesian methods in global optimization
Journal of Global Optimization, 1991The paper reviews methods which have been proposed for solving global optimization problems in the framework of the Bayesian paradigm. Three main approaches are singled out. In the first approach, called the Random Function Approach, methods are based on the idea of introducing a probabilistic model for the objective function in the form of a random ...
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