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Many real world applications  machine learning models, simulators, etc.  have multiple competing metrics that define performance; these require practitioners to carefully consider potential tradeoffs. However, assessing and ranking this tradeoff is nontrivial, especially when the number of metrics is more than two. Often times, practitioners scalarize the metrics into a single objective, e.g., using a weighted sum.
In this talk, we pose this problem as a constrained multiobjective optimization problem. By setting and updating the constraints, we can efficiently explore only the region of the Pareto efficient frontier of the model/system of most interest. We motivate this problem with the application of an experimental design setting, where we are trying to fabricate high performance glass substrate for solar cell panels.
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