Editorial: Advice to Machine Learning Authors

TLDR: This editorial contains suggestions to authors of papers in the area of machine learning, although much of it applies to the broader field of artificial intelligence.

Abstract: This editorial contains suggestions to authors of papers in the area of machine learning, although much of it applies to the broader field of artificial intelligence. I have distilled these comments from my five-year experience as an editor of Machine Learning, focusing on problems that tended to recur in different papers. Many comments are slanted toward papers that describe running systems, but others will be useful for different types of papers. Authors should focus on those suggestions relevant to their own research emphasis. I have divided the suggestions into a number of categories, which should be self-explanatory. I expect most readers will agree with many of the points, but undoubtedly some will be more controversial. Despite this, I believe that listing them explicitly in this manner will at least encourage authors to think about the issues before drafting their papers, and thus reduce the need for revisions at later dates.