Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features.

In many scientific applications, we observe a system in different conditions in which its components may change, rather than in isolation.

Gaussian mixture models (GMMs) are fundamental to machine learning due to their flexibility as approximating densities. However, uncertainty quantification of GMMs remains a challenge as differential entropy lacks a closed form.

Leveraging this flexibility, we implement 47 novel MCBO algorithms and benchmark them against seven existing MCBO solvers and five standard black-box optimization algorithms on ten tasks, conducting over 4000 experiments.