A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.
Machine learning as a tool to engineer microstructures: Morphological prediction of tannin-based colloids using bayesian surrogate models.MRS Bulletin, 47(1):29–37
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Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences
A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.