Poster

Robust Multi-Objective Bayesian Optimization Under Input Noise

Samuel Daulton · Sait Cakmak · Maximilian Balandat · Michael A Osborne · Enlu Zhou · Eytan Bakshy

Hall E #737

Keywords: [ OPT: Global Optimization ] [ OPT: Non-Convex ] [ PM: Gaussian Processes ] [ OPT: Multi-objective Optimization ] [ OPT: Optimization and Learning under Uncertainty ] [ OPT: Zero-order and Black-box Optimization ]

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Wed 20 Jul 3:30 p.m. PDT — 5:30 p.m. PDT
 
Spotlight presentation: Deep Learning/Optimization
Wed 20 Jul 1:30 p.m. PDT — 3 p.m. PDT

Abstract:

Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input noise, resulting in a product that is often less performant than expected. Although BO methods have been proposed for optimizing a single objective under input noise, no existing method addresses the practical scenario where there are multiple objectives that are sensitive to input perturbations. In this work, we propose the first multi-objective BO method that is robust to input noise. We formalize our goal as optimizing the multivariate value-at-risk (MVaR), a risk measure of the uncertain objectives. Since directly optimizing MVaR is computationally infeasible in many settings, we propose a scalable, theoretically-grounded approach for optimizing MVaR using random scalarizations. Empirically, we find that our approach significantly outperforms alternative methods and efficiently identifies optimal robust designs that will satisfy specifications across multiple metrics with high probability.

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