1 citations · 1 across the 3 of their papers we have counts for
4 papers
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
Chao Jiang, Yueling Huang, Miqing Li
Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is to adequatel…
When to Use Which? Benchmarking Optimisers for Configurable Systems under Varying Budgets
Chao Jiang, Yulong Ye, Tao Chen +1
Software configuration tuning is crucial for optimising system performance, and various optimisers have emerged over the last decade. Yet, the time required during the tuning proce…
Do We Really Need to Approach the Entire Pareto Front in Many-Objective Bayesian Optimisation?
Chao Jiang, Jingyu Huang, Miqing Li
Many-objective optimisation, a subset of multi-objective optimisation, involves optimisation problems with more than three objectives. As the number of objectives increases, the nu…
Revealing Domain-Spatiality Patterns for Configuration Tuning: Domain Knowledge Meets Fitness Landscapes
Yulong Ye, Hongyuan Liang, Chao Jiang +2
Configuration tuning for better performance is crucial in quality assurance. Yet, there has long been a mystery on tuners' effectiveness, due to the black-box nature of configurabl…