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%2C%20a%20framework%20leveraging%20multi-objective%20Bayesian%20Optimization%20to%20explore%20fleets%20on%20the%20Pareto%20front%20of%20performance%20and%20cost%20while%20accounting%20for%20uncertainty%20in%20the%20design%20space.%20Moreover%2C%20we%20establish%20a%20sub-linear%20bound%20for%20cumulative%20regret%2C%20supporting%20BOFD's%20robustness%20and%20efficacy.%20Extensive%20benchmark%20experiments%20in%20synthetic%20and%20simulated%20environments%20demonstrate%20the%20superiority%20of%20our%20framework%20over%20state-of-the-art%20methods%2C%20achieving%20efficient%20fleet%20designs%20with%20minimal%20fleet%20evaluations.)
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