A parametric multi-objective Bayesian optimizer amortizes optimization across continuous task spaces by alternating generative solution sampling and acquisition-driven search to enable direct prediction for unseen problems without re-evaluations.
Random hypervolume scalarizations for provable multi-objective black box optimization
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Amortized Multi-Objective Optimization Across Tasks with Generative Solution Modeling
A parametric multi-objective Bayesian optimizer amortizes optimization across continuous task spaces by alternating generative solution sampling and acquisition-driven search to enable direct prediction for unseen problems without re-evaluations.