RPM-BO combines random projection with a learned, semi-supervised manifold mapping to run Bayesian optimization in a low-dimensional space and projects candidates back to the original space.
Journal of Machine Learning Research13(10), 281–305 (2012)
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High-Dimensional Bayesian Optimization via Random Projection of Manifold Subspaces
RPM-BO combines random projection with a learned, semi-supervised manifold mapping to run Bayesian optimization in a low-dimensional space and projects candidates back to the original space.