  Bayesian optimization works effectively optimizing parameters in black-box
problems. However, this method did not work for high-dimensional parameters in
limited trials. Parameters can be efficiently explored by nonlinearly embedding
them into a low-dimensional space; however, the constraints cannot be
considered. We proposed combining parameter decomposition by introducing
disentangled representation learning into nonlinear embedding to consider both
known equality and unknown inequality constraints in high-dimensional Bayesian
optimization. We applied the proposed method to a powder weighing task as a
usage scenario. Based on the experimental results, the proposed method
considers the constraints and contributes to reducing the number of trials by
approximately 66% compared to manual parameter tuning.

Machine Learning