ML Reproducibility

Machine learning reproducibility asks whether reported results can be recovered from the data, code, configurations, and execution environments—and whether they hold beyond one run. Common approaches include releasing versioned artifacts and provenance, standardizing benchmark splits and metrics, controlling seeds and numerical computation, and using independent replications with multi-seed uncertainty estimates. These methods are applied across settings such as LLM inference, biomedical prediction, recommender systems, and robotics, where results can shift with hardware, preprocessing, evaluation protocols, or data choice. A central tension is that deterministic reruns and executable code do not guarantee faithful reproduction or robust conclusions, while thorough audits and repeated experiments can be costly.