<ul><li>Bayesian Optimization (BO) is a method for optimizing expensive-to-evaluate black-box functions.</li><li>Traditional BO assumes full control over query variables, but in real-world scenarios, controlling certain variables may incur costs.</li><li>This problem is known as Bayesian Optimization with cost-varying variable subsets (BOCVS).</li><li>A new algorithm is proposed for BOCVS with random and unknown costs, achieving sub-linear rate in quality and cost regret.</li></ul>

Bayesian Optimization for Unknown Cost-Varying Variable Subsets with No-Regret Costs

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