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Gustavo Malkomes · Harvey Cheng · Michael McCourt

Many problems in engineering and design require balancing competing objectives under the presence of uncertainty. The standard approach in the literature characterizes the relationship between design decisions and their corresponding outcomes as a Pareto frontier, which is discovered through multiobjective optimization. In this position paper, we suggest that this approach is not ideal for reasoning about practical design decisions. Instead of multiobjective optimization, we propose soliciting desired minimum performance constraints on all objectives to define regions of satisfactory. We present work-in-progress which visualizes the design decisions that consistently satisfy user-defined thresholds in an additive manufacturing problem.

Author Information

Gustavo Malkomes (SigOpt)
Harvey Cheng (SigOpt)

I am a research engineer at SigOpt. Currently, I am interested in problems in Bayesian optimization and reinforcement learning. I obtained my Ph.D. in electrical engineering from Princeton University, where I was advised by Prof. Warren B. Powell. My doctoral studies focused on approximate dynamic programming, stochastic optimization, and optimal learning, with an application in managing grid-level battery storage.

Michael McCourt (SigOpt)

Research interests include efficient global optimization, approximation theory with reproducing kernel Hilbert spaces, and computational statistics/mathematics. Currently a member of SigOpt, providing efficient experimental designs to customers in financial services, insurance models, and machine learning research, as well as any other projects that want to efficiently optimize their performance.

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