Design and Analysis of the NIPS 2016 Review Process

TL;DR

This paper analyzes the review process of NIPS 2016, focusing on data-driven insights and experiments to evaluate and improve the peer-review system for large-scale academic conferences.

Contribution

It provides a comprehensive analysis of the NIPS 2016 review process and investigates the effectiveness of collecting ordinal rankings from reviewers.

Findings

Review process data analysis reveals strengths and weaknesses.

Ordinal rankings may improve review quality.

Insights for future conference review designs.

Abstract

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.