A new acquisition function for Bayesian optimization based on the moment-generating function
By Hao Wang, Niki van Stein, Michael T. M. Emmerich, Thomas Bäck. 2017
78 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
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
- Enrique Alba
- Andrés Camero
- Thomas Bäck
- Michael T. M. Emmerich
- Yaochu Jin
- Zwe-Lee Gaing
- Xing-yi Zhang
- Jamal Toutouh
In Computational Theory and Mathematics
- Thomas Bäck
- Enrique Alba
- Andrés Camero
- Hao Wang
- Michael T. M. Emmerich
- Yaochu Jin
- Xing-yi Zhang
- Ye Tian
Citations in the corpus
- Bayesian Neural Architecture Search using A Training-Free Performance Metric (2020, background): “Please, see [37] for an overview of the acquisition functions.”
- Cooling Strategies for the Moment-Generating Function in Bayesian Global Optimization (2018, methodology): “2) are generated from the Latin hypercube sampling with design ranges: t0 ∈ [1,5] and tf ∈ [10−3,0.”