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Found 4 papers, 1 papers with code
Sensitivity Prewarping for Local Surrogate Modeling
In the continual effort to improve product quality and decrease operations costs, computational modeling is increasingly being deployed to determine feasibility of product designs or configurations.
The Kalai-Smorodinski solution for many-objective Bayesian optimization
An ongoing aim of research in multiobjective Bayesian optimization is to extend its applicability to a large number of objectives.
On the choice of the low-dimensional domain for global optimization via random embeddings
Then, the search of solutions can be reduced to the random embedding of a low dimensional space into the original one, resulting in a more manageable optimization problem.
A warped kernel improving robustness in Bayesian optimization via random embeddings
This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel.
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