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Bayesian optimization
Bayesian optimization is a sequential design strategyfor global optimization of black-box functions that doesn't require derivatives.
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Related topics
Related topics
12 relations
Bayesian experimental design
Deep learning
Derivative-free optimization
Global optimization
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Broader (2)
Machine learning
Stochastic optimization
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
A Tutorial on Bayesian Optimization
P. Frazier
ArXiv
2018
Corpus ID: 49656213
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It…
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Review
2016
Review
2016
Taking the Human Out of the Loop: A Review of Bayesian Optimization
Bobak Shahriari
,
Kevin Swersky
,
Ziyun Wang
,
Ryan P. Adams
,
N. D. Freitas
Proceedings of the IEEE
2016
Corpus ID: 14843594
Big Data applications are typically associated with systems involving large numbers of users, massive complex software systems…
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Highly Cited
2016
Highly Cited
2016
Bayesian Optimization with Robust Bayesian Neural Networks
Jost Tobias Springenberg
,
Aaron Klein
,
S. Falkner
,
F. Hutter
NIPS
2016
Corpus ID: 14573403
Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to…
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Highly Cited
2015
Highly Cited
2015
Scalable Bayesian Optimization Using Deep Neural Networks
Jasper Snoek
,
Oren Rippel
,
+6 authors
Ryan P. Adams
ICML
2015
Corpus ID: 12604141
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies…
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Highly Cited
2014
Highly Cited
2014
Bayesian Optimization with Unknown Constraints
M. Gelbart
,
Jasper Snoek
,
Ryan P. Adams
UAI
2014
Corpus ID: 948625
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective…
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Highly Cited
2013
Highly Cited
2013
Multi-Task Bayesian Optimization
Kevin Swersky
,
Jasper Snoek
,
Ryan P. Adams
NIPS
2013
Corpus ID: 1311677
Bayesian optimization has recently been proposed as a framework for automatically tuning the hyperparameters of machine learning…
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Highly Cited
2012
Highly Cited
2012
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek
,
H. Larochelle
,
Ryan P. Adams
NIPS
2012
Corpus ID: 632197
The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters…
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Review
2010
Review
2010
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
E. Brochu
,
Vlad M. Cora
,
N. D. Freitas
ArXiv
2010
Corpus ID: 1640103
We present a tutorial on Bayesian optimization, a method of finding the maximum of expensive cost functions. Bayesian…
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Highly Cited
2008
Highly Cited
2008
Practical bayesian optimization
D. Lizotte
2008
Corpus ID: 123325420
Global optimization of non-convex functions over real vector spaces is a problem of widespread theoretical and practical interest…
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Highly Cited
1999
Highly Cited
1999
BOA: the Bayesian optimization algorithm
M. Pelikán
,
D. Goldberg
,
E. Cantú-Paz
1999
Corpus ID: 2355296
In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of…
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