Pre-registration in a nutshell

Separate the generation and confirmation of hypotheses:

   Come up with an exciting research question   

   Write a paper proposal without confirmatory experiments   

   After the paper is accepted, run the experiments and report your results   

What does science get?

  • A healthy mix of positive and negative results
  • Reasonable ideas that don’t work still get published, avoiding wasteful replications
  • Papers are evaluated on the basis of scientific interest, not whether they achieve the best results

What do you get?

  • It's easier to plan your research: get feedback before investing in lengthy experiments
  • Your research is stronger: results have increased credibility
  • Convince people that they will learn something even if the result is negative

Preregistration in more detail

What is pre-registration and how does it improve peer review? 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

A call for papers and detailed dates will be announced shortly. We are targetting NeurIPS 2021 for this workshop.

FAQs

Organisers

João F. Henriques

João F. Henriques

University of Oxford

Samuel Albanie

Samuel Albanie

University of Oxford

Michela Paganini

Michela Paganini

Facebook AI Research

Gül Varol

Gül
Varol

University of Oxford

Questions?