Design and Analysis of the NIPS 2016 Review Process
This addresses the problem of ensuring quality and efficiency in large-scale academic peer review for machine learning conferences, but it is incremental as it builds on existing review processes.
The paper tackled the challenge of assessing and improving the peer-review process for the NIPS 2016 conference, which saw massive growth in submissions, reviewers, and attendees, by analyzing review data and conducting an experiment on ordinal rankings to provide insights for future conference designs.
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning. The 2016 edition of the conference comprised more than 2,400 paper submissions, 3,000 reviewers, and 8,000 attendees. This represents a growth of nearly 40% in terms of submissions, 96% in terms of reviewers, and over 100% in terms of attendees as compared to the previous year. The massive scale as well as rapid growth of the conference calls for a thorough quality assessment of the peer-review process and novel means of improvement. In this paper, we analyze several aspects of the data collected during the review process, including an experiment investigating the efficacy of collecting ordinal rankings from reviewers. Our goal is to check the soundness of the review process, and provide insights that may be useful in the design of the review process of subsequent conferences.