List Replicability

List replicability in machine learning and statistics focuses on developing algorithms that produce consistent results across multiple runs on independent datasets, addressing the reproducibility crisis in empirical science. Current research investigates this concept across various learning paradigms, including PAC learning, reinforcement learning, and clustering, often employing techniques like list global stability and exploring trade-offs between computational efficiency and replicability guarantees. Achieving high replicability is crucial for enhancing the reliability and trustworthiness of scientific findings and improving the generalizability of machine learning models in practical applications.