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One of the challenges in machine learning research is to ensure thatpresented and published results are sound and reliable. Reproducibility, thatis obtaining similar results as presented in a paper or talk, using the samecode and data (when available), is a necessary step to verify the reliabilityof research findings. Reproducibility is also an important step to promote openand accessible research, thereby allowing the scientific community to quicklyintegrate new findings and convert ideas to practice. Reproducibility alsopromotes the use of robust experimental workflows, which potentially reduceunintentional errors. In 2019, the Neural Information Processing Systems(NeurIPS) conference, the premier international conference for research inmachine learning, introduced a reproducibility program, designed to improve thestandards across the community for how we conduct, communicate, and evaluatemachine learning research. The program contained three components: a codesubmission policy, a community-wide reproducibility challenge, and theinclusion of the Machine Learning Reproducibility checklist as part of thepaper 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 thisinitiative.

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