Position: Embracing Negative Results in Machine Learning

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. Note from the authors: Have some of our publications been rejected due to lacking competitive results and has this been frustrating at times? Yes. However, the following position paper is not a personal vendetta: we truly believe embracing negative results can be an asset for the machine learning research community and want to present an objective deliberation on why. We hope to convince you, the reader, of the same in the following pages and spark discussion as well as change in our community. active researchers and funding volume (Maslej et al., 2023; Krenn et al., 2023) . There are many machine learning publications that provide value for the research community: works centered around theory and proofs, benchmarks, survey papers and position papers. However, a large number of machine learning publications examine a (often novel) method and then demonstrate its performance on relevant problems; these are the types of publications we focus on in this work.