Generalized Label Shift

Generalized Label Shift (GLS) addresses the challenge of machine learning when the relationship between features and labels differs between training and test data, going beyond simpler label proportion shifts. Current research focuses on developing robust methods for correcting GLS, including kernel-based approaches and those leveraging self-supervised learning to refine feature representations, often framed within a hypothesis testing or distribution matching framework. These advancements aim to improve the generalization and reliability of machine learning models in real-world scenarios where data distributions inevitably change, impacting diverse fields from biology to astrophysics. The ultimate goal is to build more robust and adaptable models that can effectively transfer knowledge across varying data conditions.