  Publications proposing novel machine learning methods are often primarily
rated by exhibited predictive performance on selected problems. In this
position paper we argue that predictive performance alone is not a good
indicator for the worth of a publication. Using it as such even fosters
problems like inefficiencies of the machine learning research community as a
whole and setting wrong incentives for researchers. We therefore put out a call
for the publication of "negative" results, which can help alleviate some of
these problems and improve the scientific output of the machine learning
research community. To substantiate our position, we present the advantages of
publishing negative results and provide concrete measures for the community to
move towards a paradigm where their publication is normalized.
