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Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR
13 references·1 citing·45 second hop
Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR (2024)Improving Reproducibility in AI Research:…The fundamental principles of reproducibility (2021)The fundamental principles of…Do machine learning platforms provide out-of-the-box reproducibility? (2021)Do machine learning platforms…Threats of a replication crisis in empirical computer science (2020)Threats of a replication cris…The Reproducibility Crisis Is Real (2020)The Reproducibility Crisis Is…Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program) (2020)Improving Reproducibility in …Standing on the Feet of Giants — Reproducibility in AI (2019)Standing on the Feet of Giant…Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Statistical and Machine Learn…State of the Art: Reproducibility in Artificial Intelligence (2018)State of the Art: Reproducibi…Reproducibility in Scientific Computing (2018)Reproducibility in Scientific…On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications (2018)On Reproducible AI: Towards R…Why Most Published Research Findings Are False (2005)Why Most Published Research F…Are Structured Abstracts Easier to Read Than Traditional Ones? (1997)Are Structured Abstracts Easi…Logik der Forschung (1935)Logik der ForschungHow can we make sound replication decisions? (2025)How can we make sound replica…Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement (1999)Meta-analysis of Observational Studies in Epidemiology A Proposal for Reporting (2000)The CONSORT statement: revised recommendations for improving the quality of reports of parallel-group randomised trials (2001)False-Positive Psychology (2011)Power failure: why small sample size undermines the reliability of neuroscience (2013)Estimating the reproducibility of psychological science (2015)Believe it or not: how much can we rely on published data on potential drug targets? (2011)Reproducible Research in Computational Science (2011)Redefine statistical significance (2017)Applications of deep learning in stock market prediction: Recent progress (2021)Reproducibility in machine learning for health research: Still a ways to go (2021)Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Another look at measures of forecast accuracy (2006)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Forecasting: theory and practice (2022)Why Most Published Research Findings Are False (2005)The Logic of Scientific Discovery (2013)Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv (2019)A taxonomy for reproducible and replicable research in environmental modelling (2020)The data science life cycle (2020)1,500 scientists lift the lid on reproducibility (2016)Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators (2023)Rethinking open source generative AI: open washing and the EU AI Act (2024)Promoting an open research culture (2015)A manifesto for reproducible science (2017)A large-scale study on research code quality and execution (2022)The Fallacy of AI Functionality (2022)REFORMS: Consensus-based Recommendations for Machine-learning-based Science (2024)The file drawer problem and tolerance for null results. (1979)Confirmation Bias: A Ubiquitous Phenomenon in Many Guises (1998)A Critique of Electrodermal Activity Practices at CHI (2021)Personalization Improves Gamification (2021)Conjectures and Refutations: The Growth of Scientific Knowledge. (1964)Conjectures and Refutations: The Growth of Scientific Knowledge. (1965)Statistics and Chemometrics for Analytical Chemistry (2005)Reproducibility in machine‐learning‐based research: Overview, barriers, and drivers (2025)What is reproducibility in artificial intelligence and machine learning research? (2025)A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows (2023)Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements (2023)Reproducible research policies and software/data management in scientific computing journals: a survey, discussion, and perspectives (2025)History of Science and Its Rational Reconstructions (1970)Logic of Discovery or Psychology of Research? (1970)Causality and Explanation (1998)
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References

  • 2005Why Most Published Research Findings Are False10,471 cited
  • 1935Logik der Forschung1,552 cited
  • 2018Statistical and Machine Learning forecasting methods: Concerns and ways forward1,515 cited
  • 2018State of the Art: Reproducibility in Artificial Intelligence292 cited
  • 2020Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)228 cited
  • 2020Threats of a replication crisis in empirical computer science158 cited
  • 2018Reproducibility in Scientific Computing149 cited
  • 2018On Reproducible AI: Towards Reproducible Research, Open Science, and Digital Scholarship in AI Publications99 cited
  • 2021The fundamental principles of reproducibility92 cited
  • 2021Do machine learning platforms provide out-of-the-box reproducibility?69 cited
  • 1997Are Structured Abstracts Easier to Read Than Traditional Ones?58 cited
  • 2019Standing on the Feet of Giants — Reproducibility in AI19 cited
  • 2020The Reproducibility Crisis Is Real17 cited

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  • 2025How can we make sound replication decisions?11 cited

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