Online Linear Regression

Online linear regression focuses on efficiently updating linear models as data streams in, aiming to minimize prediction error over time. Current research emphasizes developing algorithms with strong theoretical guarantees, such as optimal regret bounds under various conditions (e.g., dynamic environments, multivariate responses, and groupwise fairness constraints), often extending and refining existing methods like the Vovk-Azoury-Warmuth algorithm. These advancements are significant for handling large-scale, real-time data analysis, improving the efficiency and robustness of machine learning models in diverse applications, including forecasting and personalized recommendations.