Converge then diversify: decoupling convergence and diversity in multi-objective Bayesian optimisation.
Read the original at arxiv.org→arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In MOBO, the goal is...
Original headline: "Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation"
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- Sep 15, 04:00 UTC arXiv cs.AI lead source Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation