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Found 6 papers, 5 papers with code
Surrogate Model Based Hyperparameter Tuning for Deep Learning with SPOT
A surrogate model based hyperparameter tuning approach for deep learning is presented.
Resource Planning for Hospitals Under Special Consideration of the COVID-19 Pandemic: Optimization and Sensitivity Analysis
Reasonable default values of these parameters were obtained in detailed discussions with medical professionals.
Hospital Capacity Planning Using Discrete Event Simulation Under Special Consideration of the COVID-19 Pandemic
There are benefits for medical professionals, e. g, analysis of the pandemic at local, regional, state and federal level, the consideration of special risk groups, tools for validating the length of stays and transition probabilities.
Continuous Optimization Benchmarks by Simulation
However, predictions from data-driven models tend to be smoother than the ground-truth from which the training data is derived.
Expected Improvement versus Predicted Value in Surrogate-Based Optimization
Few results from the literature show evidence, that under certain conditions, expected improvement may perform worse than something as simple as the predicted value of the surrogate model.
In a Nutshell -- The Sequential Parameter Optimization Toolbox
The sequential parameter optimization (SPOT) package for R is a toolbox for tuning and understanding simulation and optimization algorithms.
