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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 th
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