Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

TL;DR

This paper discusses NeurIPS 2019's reproducibility program, which aimed to enhance the reliability, transparency, and robustness of machine learning research through policy changes, challenges, and checklists.

Contribution

It introduces a comprehensive reproducibility initiative at NeurIPS 2019, including policies, challenges, and checklists, and evaluates its impact on research practices.

Findings

Increased code submissions and reproducibility efforts.

Improved awareness and standards for reproducibility.

Positive feedback from the community.

Abstract

One of the challenges in machine learning research is to ensure that presented and published results are sound and reliable. Reproducibility, that is obtaining similar results as presented in a paper or talk, using the same code and data (when available), is a necessary step to verify the reliability of research findings. Reproducibility is also an important step to promote open and accessible research, thereby allowing the scientific community to quickly integrate new findings and convert ideas to practice. Reproducibility also promotes the use of robust experimental workflows, which potentially reduce unintentional errors. In 2019, the Neural Information Processing Systems (NeurIPS) conference, the premier international conference for research in machine learning, introduced a reproducibility program, designed to improve the standards across the community for how we conduct, communicate, and evaluate machine learning research. The program contained three components: a code submission policy, a community-wide reproducibility challenge, and the inclusion of the Machine Learning Reproducibility checklist as part of the paper submission process. In this paper, we describe each of these components, how it was deployed, as well as what we were able to learn from this initiative.