Multi Group Learning

Multi-group learning addresses the challenge of building machine learning models that perform well across multiple, potentially overlapping subgroups within a dataset. Current research focuses on developing efficient algorithms, particularly for scenarios with numerous or hierarchically structured groups, often employing techniques adapted from online learning and active learning to improve sample efficiency and reduce computational cost. This field is crucial for addressing issues like fairness and hidden stratification in data, ensuring reliable model performance across diverse subpopulations and improving the generalizability and trustworthiness of machine learning models in real-world applications.