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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 designDeep learningDerivative-free optimizationGlobal optimization

Broader (2)

Machine learningStochastic optimization

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2019
Highly Cited
2019
Bayesian Optimization Meets Bayesian Optimal Stopping
Bayesian optimization (BO) is a popular paradigm for optimizing the hyperparameters of machine learning (ML) models due to its… 
2019
2019
Offline Contextual Bayesian Optimization
In black-box optimization, an agent repeatedly chooses a configuration to test, so as to find an optimal configuration. In many… 
2018
2018
Bayesian Optimization for Dynamic Problems
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using… 
Highly Cited
2017
Highly Cited
2017
GPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow… 
2016
2016
Bayesian Hyperparameter Optimization for Ensemble Learning
In this paper, we bridge the gap between hyperparameter optimization and ensemble learning by performing Bayesian optimization of… 
Highly Cited
2015
Highly Cited
2015
Bayesian Optimization with Exponential Convergence
This paper presents a Bayesian optimization method with exponential convergence without the need of auxiliary optimization and… 
Highly Cited
2015
Highly Cited
2015
Unbounded Bayesian Optimization via Regularization
Bayesian optimization has recently emerged as a popular and efficient tool for global optimization and hyperparameter tuning… 
Highly Cited
2014
Highly Cited
2014
An Entropy Search Portfolio for Bayesian Optimization
Bayesian optimization is a sample-efficient method for black-box global optimization. How- ever, the performance of a Bayesian… 
Highly Cited
2014
Highly Cited
2014
Using trajectory data to improve bayesian optimization for reinforcement learning
Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in several challenging… 
Highly Cited
2010
Highly Cited
2010
Batch Bayesian Optimization via Simulation Matching
Bayesian optimization methods are often used to optimize unknown functions that are costly to evaluate. Typically, these methods… 
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