2 research outputs found
Distributionally Robust Bayesian Optimization
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.Comment: Accepted at AISTATS 202