  Amid mounting concern about the reliability and credibility of machine
learning research, we present a principled framework for making robust and
generalizable claims: the multiverse analysis. Our framework builds upon the
multiverse analysis (Steegen et al., 2016) introduced in response to
psychology's own reproducibility crisis. To efficiently explore
high-dimensional and often continuous ML search spaces, we model the multiverse
with a Gaussian Process surrogate and apply Bayesian experimental design. Our
framework is designed to facilitate drawing robust scientific conclusions about
model performance, and thus our approach focuses on exploration rather than
conventional optimization. In the first of two case studies, we investigate
disputed claims about the relative merit of adaptive optimizers. Second, we
synthesize conflicting research on the effect of learning rate on the large
batch training generalization gap. For the machine learning community, the
multiverse analysis is a simple and effective technique for identifying robust
claims, for increasing transparency, and a step toward improved
reproducibility.
