  In the early 2010s, a crisis of reproducibility rocked the field of
psychology. Following a period of reflection, the field has responded with
radical reform of its scientific practices. More recently, similar questions
about the reproducibility of machine learning research have also come to the
fore. In this short paper, we present select ideas from psychology's
reformation, translating them into relevance for a machine learning audience.
