Response Surface Methodology for Optimizing Hyper Parameters
By Claus Weihs, Karsten Luebke, Irina Czogiel. 2006
12 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Martin Wistuba
- Lars Schmidt-Thieme
- Nicolas Schilling
- Xin He
- Kaiyong Zhao
- Xiaowen Chu
- Marco F. Huber
- Marc-André Zöller
In Management Science and Operations Research
- Lars Schmidt-Thieme
- Xin He
- Kaiyong Zhao
- Nicolas Schilling
- Martin Wistuba
- Xiaowen Chu
- Julian McAuley
- Steffen Rendle
In Statistics and Probability
Citations in the corpus
- AutoML: A Survey of the State-of-the-Art (2019, methodology): “At the beginning of the study on hyperparameters search, grid search [93]–[95] is one of the most popular methods as it is simple to implement in parallel and tends to find as good as or better hyperparameters than manual search in the”
- Scalable Gaussian process-based transfer surrogates for hyperparameter optimization (2017, methodology): “In the literature, the contours of y are also called response surface (Czogiel et al. 2006), a term which we will also use throughout the paper.”