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Bayesian optimization

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

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It… Expand
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Highly Cited
2017
Highly Cited
2017
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as… Expand
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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… Expand
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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… Expand
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Highly Cited
2015
Highly Cited
2015
Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a sub… Expand
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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… Expand
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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… Expand
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Review
2010
Review
2010
We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian… Expand
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Highly Cited
2005
Highly Cited
2005
In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of… Expand
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… Expand
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