Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation
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arXiv:2609.13396v1 Announce Type: cross 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 to adequately approximate the Pareto front; that is, to obtain a high-quality sol…
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- 2026-09-15 04:00 · arXiv stat.ML
Converge Then Diversify: Decoupling Convergence and Diversity in Multi-Objective Bayesian Optimisation