mlsanityData to 7 Oct 2026

Research Reproducibility as a Survival Analysis

arXiv 2012.09932 ↗First posted 17 Dec 2020stat.ML cs.AI cs.LGcited by 14

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Abstract (arXiv)

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival analysis problem. We argue that this perspective represents a more accurate model of the underlying meta-science question of reproducible research, and we show how a survival analysis allows us to draw new insights that better explain prior longitudinal data. The data and code can be found at https://github.com/EdwardRaff/Research-Reproducibility-Survival-Analysis

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 3; 2025: 1; 2026: 2.

14 papers.

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  1. 2021Accounting for Variance in Machine Learning Benchmarkscited by 195
  2. 2022Sources of Irreproducibility in Machine Learning: A Reviewcited by 28
  3. 2024Assemblage: Automatic Binary Dataset Construction for Machine Learningcited by 12
  4. 2023Reproducibility of Machine Learning: Terminology, Recommendations and Open Issuescited by 8
  5. 2024What Do Machine Learning Researchers Mean by "Reproducible"?cited by 8
  6. 2022Does the Market of Citations Reward Reproducible Work?cited by 7
  7. 2022A Siren Song of Open Source Reproducibilitycited by 7
  8. 2023A Coreset Learning Reality Checkcited by 4
  9. 2022Lempel-Ziv Networkscited by 3
  10. 2023Exploring the Sharpened Cosine Similaritycited by 1
  11. 2025More Rigorous Software Engineering Would Improve Reproducibility in Machine Learning Researchcited by 1
  12. 2024Generalizability of experimental studiescited by 0
2 more
  1. 2026Bayesian Meta-Analyses Could Be More: A Case Study in Trial of Labor After a Cesarean-section Outcomes and Complicationscited by 0
  2. 2026Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasetscited by 0