  In this paper, we consider the problem of Gaussian process (GP) optimization
with an added robustness requirement: The returned point may be perturbed by an
adversary, and we require the function value to remain as high as possible even
after this perturbation. This problem is motivated by settings in which the
underlying functions during optimization and implementation stages are
different, or when one is interested in finding an entire region of good inputs
rather than only a single point. We show that standard GP optimization
algorithms do not exhibit the desired robustness properties, and provide a
novel confidence-bound based algorithm StableOpt for this purpose. We
rigorously establish the required number of samples for StableOpt to find a
near-optimal point, and we complement this guarantee with an
algorithm-independent lower bound. We experimentally demonstrate several
potential applications of interest using real-world data sets, and we show that
StableOpt consistently succeeds in finding a stable maximizer where several
baseline methods fail.
