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Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
2021
International Conference on Machine Learning
Many problems in engineering design and simulation require balancing competing objectives under the presence of uncertainty. Sample-efficient multiobjective optimization methods focus on the objective function values in metric space and ignore the sampling behavior of the design configurations in parameter space. Consequently, they may provide little actionable insight on how to choose designs in the presence of metric uncertainty or limited precision when implementing a chosen design. We
dblp:conf/icml/MalkomesCLM21
fatcat:5zpy2d4qu5eexn56wun7pflxni