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arXiv · 2002.09038

Distributionally Robust Bayesian Optimization

Johannes Kirschner·Ilija Bogunovic·Stefanie Jegelka·Andreas Krause
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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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Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause. 2020-02-20. Distributionally Robust Bayesian Optimization. https://arxiv.org/abs/2002.09038

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