Benchmarks on popular datasets have played a key role in the considerable measurable progress that machine learning has made in the last few years. But reviewers can be tempted to prioritize incremental improvements in benchmarks to the detriment of other scientific criteria, destroying many good ideas in their infancy. Authors can also feel obligated to make orthogonal improvements in order to “beat the state-of-the-art”, making the main contribution hard to assess.

Pre-registration changes the incentives by reviewing and accepting a paper before experiments are conducted. The emphasis of peer-review will be on whether the experiment plan can adequately prove or disprove one (or more) hypotheses. Some results will be negative, and this is welcomed. This way, good ideas that do not work will get published, instead of filed away and wastefully replicated many times by different groups. Finally, the clear separation between hypothesizing and confirmation (absent in the current review model) will raise the statistical significance of the results.

Call for Papers: We are inviting submissions on the broad range of topics covered at NeurIPS! The paper template is structured like a mini-tutorial on the pre-registration process to get you started quickly. Pre-registered papers will be published at the workshop. Authors will then have the opportunity to submit the results paper to the Proceedings of Machine Learning Research (PMLR), a sister publication to the Journal of Machine Learning Research (JMLR). The review process for this second stage will aim to ensure that the authors have performed a good-faith attempt to complete the experiments described in their proposal paper.