What Do Machine Learning Researchers Mean by "Reproducible"?
Edward Raff, Michel Benaroch, Sagar Samtani, Andrew L. Farris·December 05, 2024
Summary
The text outlines eight key topics related to reproducibility in AI/ML research, focusing on repeatability, reproducibility, replicability, adaptability, model selection, label/data quality, meta & incentive, and maintainability. It highlights the importance of creating a dedicated track for scientific rigor at major conferences to incentivize and organize this critical work. The text also discusses challenges in repeatability, such as code errors, software version conflicts, and floating point errors, and distinguishes between instantaneous repeatability and maintainability over time.
Introduction
Background
Definition of AI/ML research
Importance of reproducibility in scientific research
Objective
To outline eight key topics related to reproducibility in AI/ML research
To emphasize the role of creating a dedicated track for scientific rigor at major conferences
Key Topics in Reproducibility
Repeatability
Challenges in achieving instantaneous repeatability
Factors contributing to code errors, software version conflicts, and floating point errors
Reproducibility
Distinction between instantaneous and maintainable reproducibility
Replicability
Importance of replicability in AI/ML research
Factors affecting replicability
Adaptability
Role of adaptability in AI/ML models
Strategies for enhancing model adaptability
Model Selection
Criteria for selecting appropriate models
Considerations in model selection for reproducibility
Label/Data Quality
Impact of data quality on AI/ML model performance
Techniques for ensuring high-quality labels and data
Meta & Incentive
Role of meta-analysis in AI/ML research
Incentives for promoting reproducibility in AI/ML research
Maintainability
Importance of maintainability in AI/ML projects
Strategies for enhancing maintainability
Challenges and Solutions
Challenges in Repeatability
Code errors
Software version conflicts
Floating point errors
Solutions for Repeatability
Best practices for code management
Use of version control systems
Strategies for handling floating point precision
Challenges in Reproducibility
Changes in software environments
Evolving data landscapes
Solutions for Reproducibility
Documentation of research processes
Use of standardized environments
Versioning of data and code
Challenges in Replicability
Variability in data sources
Differences in model training conditions
Solutions for Replicability
Sharing of datasets and models
Detailed documentation of model training processes
Challenges in Model Selection
Lack of standardized evaluation metrics
Bias in model selection
Solutions for Model Selection
Development of comprehensive evaluation frameworks
Inclusion of diverse model types
Challenges in Label/Data Quality
Inconsistencies in labeling practices
Bias in data collection
Solutions for Label/Data Quality
Rigorous labeling guidelines
Data cleaning and preprocessing techniques
Challenges in Meta & Incentive
Lack of recognition for reproducible research
Incentives for researchers
Solutions for Meta & Incentive
Promotion of reproducibility in conference tracks
Recognition systems for reproducible research
Challenges in Maintainability
Rapid evolution of AI/ML technologies
Complexity in updating models
Solutions for Maintainability
Modular design of AI/ML systems
Continuous integration and deployment practices
Conclusion
Summary of Key Topics
Recap of the eight key topics in reproducibility
Importance of Scientific Rigor
Emphasis on the role of dedicated conference tracks
Future Directions
Potential areas for further research and improvement in reproducibility
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does the text differentiate between instantaneous repeatability and maintainability over time?
What does the text suggest about the importance of creating a dedicated track for scientific rigor at major conferences?
What are the eight key topics related to reproducibility in AI/ML research mentioned in the text?
What are some of the challenges in repeatability that the text highlights?