  We propose minimum regret search (MRS), a novel acquisition function for
Bayesian optimization. MRS bears similarities with information-theoretic
approaches such as entropy search (ES). However, while ES aims in each query at
maximizing the information gain with respect to the global maximum, MRS aims at
minimizing the expected simple regret of its ultimate recommendation for the
optimum. While empirically ES and MRS perform similar in most of the cases, MRS
produces fewer outliers with high simple regret than ES. We provide empirical
results both for a synthetic single-task optimization problem as well as for a
simulated multi-task robotic control problem.
