Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes

Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy

Abstract: We consider Bayesian optimization of expensive-to-evaluate experiments that gen- erate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are encoded by a utility function that is not known in closed form but can be estimated by asking the DM to express preferences over pairs of outcome vectors. To address this problem, we develop Bayesian op- timization with preference exploration, a novel framework that alternates between interactive real-time preference learning with the DM via pairwise comparisons between outcomes, and Bayesian optimization with a learned compo- sitional model of DM utility and outcomes. Within this framework, we propose preference exploration strategies specifically designed for this task, and demonstrate their performance via extensive simulation studies.