A dual-ranking strategy improves offline data-driven multi-objective optimization by prioritizing solutions that score well on both predicted performance and low uncertainty across different surrogate models.
Offline Model-Based Optimization: Compre- hensive Review
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COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.
citing papers explorer
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Uncertainty-Aware Offline Data-Driven Multi-Objective Optimization
A dual-ranking strategy improves offline data-driven multi-objective optimization by prioritizing solutions that score well on both predicted performance and low uncertainty across different surrogate models.
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Conservative quantum offline model-based optimization
COM-QEL integrates conservative objective models with quantum extremal learning to produce more reliable solutions than standard QEL on offline benchmark optimization tasks.