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.
IEEE Robotics & Automation Magazine27, 33–45 (2019) 16 Nguyen et al
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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.