Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization

Rose Guay-Hottin, Lison Kardassevitch, Hugo Pham, Guillaume Lajoie, Marco Bonizzato

Abstract: In diverse fields of application, Bayesian Optimization (BO) has been proposed to find the optimum of black-box functions, surpassing human-driven searches. BO’s appeal lies in its data efficiency, making it suitable for optimizing costly-to-evaluate functions without requiring extensive training data. While BO can perform well in closed-loop, domain experts frequently have hypotheses about which parameter combinations are more likely to yield optimal results. Hence, for BO to be truly relevant and adopted by practitioners, such prior knowledge needs to be efficiently and seamlessly integrated into the optimization framework. Some methods were recently developed to address this challenge, but they suffer from robustness issues when provided erroneous insight. Building on the idea of element-wise prior-weighted acquisition function, we propose to use a fixed-weight effective prior that distills expert user knowledge with minimal computational cost. Comprehensive investigation across diverse task conditions and prior quality levels revealed that our method, α$\alpha$-π$\pi$BO, surpasses Vanilla BO when provided with insights of good quality while maintaining robustness against misleading information. Moreover, unlike other methods, α$\alpha$-π$\pi$BO typically requires no hyperparameter tuning, largely simplifying its implementation in diverse tasks.