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

• URL: http://arxiv.org/abs/2003.12206v4
• Date: Wed, 30 Dec 2020 21:32:34 GMT
• Title: Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)
• Authors: Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivi\`ere, Alina Beygelzimer, Florence d'Alch\'e-Buc, Emily Fox, Hugo Larochelle
• Abstract summary: Reproducibility is obtaining similar results as presented in a paper or talk, using the same code and data (when available) In 2019, the Neural Information Processing Systems (NeurIPS) conference introduced a program, designed to improve the standards across the community for how we conduct, communicate, and evaluate machine learning research. In this paper, we describe each of these components, how they were deployed, as well as what we were able to learn from this initiative.
• Score: 43.55295847227261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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