Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2003.12206v4)

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.

Improving Reproducibility in Machine Learning Research

In this paper, Pineau et al. address significant challenges in the reproducibility of ML research. The authors examine reproducibility failures and their impact on research reliability, presenting strategies implemented during the Neural Information Processing Systems (NeurIPS) 2019 conference. This work is both a call to action and a guide, focusing on establishing methods to ensure more reliable machine learning practices.

Core Challenges and Initiatives

The remarkable increase in computational research within ML demands robust frameworks to validate experimental findings. The authors identify key factors undermining reproducibility, notably the lack of data accessibility, model specification issues, unavailability of code, inadequate metrics, and insufficient statistical rigor. Such problems suggest an impending reliance on anecdotal successes over systematic verification, which can lead to potentially erroneous conclusions being propagated as scientific truths.

One of the major steps undertaken by NeurIPS 2019 to tackle these issues was the introduction of a structured reproducibility program. This program was bifurcated into three components:

1. Code Submission Policy: While not mandatory, authors of accepted papers were expected to submit their code by the final camera-ready deadline. This approach underscored voluntary participation, recognizing proprietary and infrastructure limitations in certain instances. The submission of code acts as a vital resource, allowing the scientific community to validate and extend previous findings effectively.
2. Reproducibility Challenge: This initiative invited independent reproduction of experiments from accepted NeurIPS papers. A total of 173 papers were selected for this detailed exploration. The increased number of participating institutions, including both academic and industrial entities, speaks to the appeal and perceived value of independent reproducibility efforts.
3. Machine Learning Reproducibility Checklist: Authors submitted their work alongside a checklist detailing the experimental setup, data, and code availability. This checklist served to educate and prompt authors towards a more detailed methodological description, facilitating easier reproduction and verification by future researchers.

Outcomes and Observations

The implemented measures, particularly the NeurIPS code submission expectations, saw code availability for accepted papers rise substantially, reflected in the 74.4% compliance rate. Reviewers reported code as useful, providing additional context during the assessment process and bolstering their decision-making confidence for acceptance rates. It stands to reason that clear guidance through such structured frameworks could encourage better practices both at and beyond conferences.

Numerous exploratory analyses, as conveyed in this research, underscore the importance of transparency and collaboration. Reports suggest that reproducing results elevates understanding and mitigates over-claiming and adaptive overfitting issues. Furthermore, the growing adherence to reproducibility norms—evident in increasing participation and compliance—suggests a cultural shift within the ML community toward embracing these systemic improvements.

Implications and Future Directions

The initiatives by NeurIPS are indicative of broader trends in AI and related fields, rallying the community around reproducibility. This paper lays a pragmatic foundation that can spark changes across various domains in computational research. Further studies would benefit from assessing long-term impacts on generalization and robustness, examining which incentive structures best elevate participation and quality, and scrutinizing checklist accuracy and its effects on paper clarity and reviewer efficiency.

Pineau et al. accentuate an ongoing evolution in scientific practices concerning reproducibility. Their work offers a promising blueprint that could scaffold future methodological advancements, consolidating ML research's foundation for open and verifiable science. The systemic approach in setting precedents for artifact sharing not only augments transparency but also propels the scientific discourse into a novel paradigm where reproducibility is no longer an afterthought but rather an integral component of research integrity.