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Yue-Ling Huang

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#artificial intelligence Preprint Sep 2026

Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation

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 adequately approximate the Pareto front; that is, to obtain a high-quality solution set with 1) good convergence (closeness to the Pareto front) and...

Chao Jiang, Yue-Ling Huang, Mi-Qing Li · 0 citations

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