Structured Regression

Structured regression focuses on developing and applying regression models that incorporate prior knowledge or constraints about the relationships between variables, aiming for improved prediction accuracy and interpretability. Current research emphasizes extensions to handle complex data structures like time series and high-dimensional data, often employing ensemble methods like combined regression strategies (COBRA) and Bayesian approaches, including semi-structured models that blend interpretable statistical components with flexible neural networks. These advancements are significant for addressing challenges in diverse fields, such as fairness assessment in AI, survival analysis, and large-scale recommender systems, by enabling more accurate and reliable predictions while maintaining model interpretability.