2022/04/11 by Jixiang Qing, Qing, Jixiang, Henry B. Moss +5 · 1 citation
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #FOS: Mathematics #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2204.05411
openalex publication_date 2022/04/11 · openalex created_date 2022/04/15 · openalex updated_date 2026/07/28
We present Parallel Feasible Pareto Frontier Entropy Search (\PF\2ES) -- a novel information-theoretic acquisition function for multi-objective Bayesian optimization supporting unknown constraints and batch query. Due to the complexity of characterizing the mutual information between candidate evaluations and (feasible) Pareto frontiers, existing approaches must either employ crude approximations that significantly hamper their performance or rely on expensive inference schemes that substantially increase the optimization's computational overhead. By instead using a variational lower bound, \PF\2ES provides a low-cost and accurate estimate of the mutual information. We benchmark \PF\2ES against other information-theoretic acquisition functions, demonstrating its competitive performance for optimization across synthetic and real-world design problems.