Title: Incorporating Expert Priors into Bayesian Optimization via Dynamic Mean Decay
Primary area: probabilistic methods (Bayesian methods, variational inference, sampling, UQ, etc.)
Keywords: Bayesian optimization, Gaussian processes, Expert prior knowledge, Hyperparameter optimization

Abstract: Bayesian optimization (BO) is a powerful approach for black-box optimization, and in many real-world problems, domain experts possess valuable prior knowledge about promising regions of the search space. However, existing prior-informed BO methods are often overly complex, tied to specific acquisition functions, or highly sensitive to inaccurate priors. We propose DynMeanBO, a simple and general framework that incorporates expert priors into the Gaussian process mean function with a dynamic decay mechanism. This design allows BO to exploit expert knowledge in the early stages while gradually reverting to standard BO behavior, ensuring robustness against misleading priors while retaining the exploratory behavior of standard BO. DynMeanBO is broadly compatible with acquisition functions, introduces negligible computational cost, and comes with convergence guarantees under Expected Improvement and Upper Confidence Bound. Experiments on synthetic benchmarks and hyperparameter optimization tasks show that DynMeanBO accelerates convergence with informative priors and remains robust under biased ones.

TL;DR: We introduce DynMeanBO, a simple and general BO framework that dynamically incorporates expert priors, ensuring both faster convergence and robustness to inaccurate knowledge.