Reduced Rank

Reduced-rank regression focuses on simplifying complex data by representing it in a lower-dimensional space, thereby improving efficiency and interpretability while mitigating the effects of noise and high dimensionality. Current research emphasizes developing efficient algorithms, such as randomized methods and those incorporating unbalanced optimal transport, to solve reduced-rank regression problems in various contexts, including vector-valued regression and multi-objective policy learning. These advancements are proving valuable in diverse fields, enhancing the accuracy and scalability of machine learning models for applications ranging from social intervention analysis to neuroscience and spatial transcriptomics. The resulting improvements in model performance and interpretability are driving significant progress across numerous scientific disciplines.