Bayesian optimization

Bayesian optimization is a sequential design strategyfor global optimization of black-box functions that doesn't require derivatives.
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Papers overview

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Review
2016
Review
2016
Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems… (More)
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Highly Cited
2015
Highly Cited
2015
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies… (More)
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Highly Cited
2014
Highly Cited
2014
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective… (More)
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Highly Cited
2013
Highly Cited
2013
Bayesian optimization has recently been proposed as a framework for automatically tuning the hyperparameters of machine learning… (More)
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Highly Cited
2012
Highly Cited
2012
The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters… (More)
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Highly Cited
2011
Highly Cited
2011
Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is… (More)
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Highly Cited
2006
Highly Cited
2006
The hierarchical Bayesian optimization algorithm (hBOA) solves nearly decomposable and hierarchical optimization problems… (More)
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Highly Cited
2002
Highly Cited
2002
In recent years, several researchers have concentrated on using probabilistic models in evolutionary algorithms. These Estimation… (More)
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Highly Cited
2002
Highly Cited
2002
To solve a wide range of different problems, the research in black-box optimization faces several important challenges. One of… (More)
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Highly Cited
1999
Highly Cited
1999
In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of… (More)
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