Distributionally Robust Bayesian Optimization

• URL: http://arxiv.org/abs/2002.09038v3
• Date: Sun, 22 Mar 2020 10:40:30 GMT
• Title: Distributionally Robust Bayesian Optimization
• Authors: Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause
• Abstract summary: We present a novel distributionally robust Bayesian optimization algorithm (DRBO) for zeroth-order, noisy optimization. Our algorithm provably obtains sub-linear robust regret in various settings. We demonstrate the robust performance of our method on both synthetic and real-world benchmarks.
• Score: 121.71766171427433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robustness to distributional shift is one of the key challenges of contemporary machine learning. Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate. In this paper, we study such a problem when the distributional shift is measured via the maximum mean discrepancy (MMD). For the setting of zeroth-order, noisy optimization, we present a novel distributionally robust Bayesian optimization algorithm (DRBO). Our algorithm provably obtains sub-linear robust regret in various settings that differ in how the uncertain covariate is observed. We demonstrate the robust performance of our method on both synthetic and real-world benchmarks.

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