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The optimization of expensive to evaluate, black-box, mixed-variable functions, i.e. functions that have continuous and discrete inputs, is a difficult and yet pervasive problem in science and engineering. In Bayesian optimization (BO), special cases of this problem that consider fully continuous or fully discrete domains have been widely studied. However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications. Indoi:10.24963/ijcai.2020/361 dblp:conf/ijcai/LuoHG20 fatcat:ql5jucnuqzaxvk2fcecnn2jf5m