Robust Gaussian Process-Based Global Optimization Using a Fully Bayesian Expected Improvement Criterion
By Romain Benassi, Julien Bect, Emmanuel Vazquez. 2011
77 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Frank Hutter
- Nando de Freitas
- Ziyun Wang
- Sergey Levine
- Kevin Swersky
- Bobak Shahriari
- Quoc V. Le
- Ryan P. Adams
In Computational Theory and Mathematics
- Ziyun Wang
- Nando de Freitas
- Frank Hutter
- Bobak Shahriari
- Kevin Swersky
- Ryan P. Adams
- Aaron Klein
- Stefan Falkner
In Management Science and Operations Research
- Ziyun Wang
- Frank Hutter
- Bobak Shahriari
- Kevin Swersky
- Ryan P. Adams
- Nando de Freitas
- Aaron Klein
- José Miguel Hernández-Lobato
Citations in the corpus
- Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “Estimating the hyperparameters of GP kernels with very few function evaluations is a challenging task, often with disastrous consequences as illustrated by a simple example in [15].”
- Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters (2014, background): “However, estimating the hyper-parameters of the Gaussian process kernel with very few objective function evaluations is a daunting task, often with disastrous results as illustrated by a simple example in [2].”