AI RESEARCH
Multi-Objective Bayesian Optimization via Adaptive \varepsilon-Constraints Decomposition
arXiv CS.LG
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ArXi:2604.15959v1 Announce Type: new Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing expensive black-box functions with multiple objectives. However, existing MOBO methods often struggle with coverage, scalability with respect to the number of objectives, and integrating constraints and preferences. In this work, we propose \textit{STAGE-BO, Sequential Targeting Adaptive Gap-Filling $\varepsilon$-Constraint Bayesian Optimization}, that explicitly targets under-explored regions of the Pareto front.