A0685
Title: Penalized additive Gaussian process for auto-tuning of quantitative and qualitative factors in black-box systems
Authors: Qian Xiao - Shanghai Jiao Tong University (China) [presenting]
Abstract: Factor screening and optimization of both quantitative and qualitative (QQ) factors are critical in several recent applications where evaluating black-box systems is resource-intensive or time-consuming. Moreover, some qualitative factors in QQ may involve many levels. Yet, most current screening methods focus on factors but cannot identify important qualitative levels. To address these challenges, a novel penalized additive Gaussian process (PAGP) is proposed, featuring an interpretable additive covariance structure for QQ factors. It allows for sparsity penalties on the hyper-parameters of the covariance structure, which enables the identification of important qualitative levels. A tailored alternating direction method of multipliers is developed to optimize the L1 regularized likelihood, and a gradient-informed optimization approach using derivative information is proposed to accelerate PAGP modeling. An effective approach is further established, leveraging Shapley values for screening quantitative factors. Then, a Bayesian optimization (BO) approach leveraging the desirable uncertainty quantification of PAGP is proposed to optimize black-box systems with QQ factors. This PAGP-based Bayesian optimization can provide an interpretable importance attribution of factor levels during optimization. Simulations and real case studies illustrate the superior performance of the proposed methods compared to some state-of-the-art approaches.
