Perspectives on Machine Learning from Psychology's Reproducibility Crisis
This addresses the issue of reproducibility for machine learning researchers, but it is incremental as it adapts existing ideas from another field.
The paper tackles the problem of reproducibility in machine learning research by translating ideas from psychology's reforms following its reproducibility crisis, aiming to improve scientific practices in the field.
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.