Online Regression

Online regression focuses on building and updating predictive models incrementally as new data arrives, addressing the limitations of batch methods in dynamic environments. Current research emphasizes efficient algorithms like online logistic regression and those leveraging Gaussian processes or kernel methods, often incorporating techniques like selective sampling and robust loss functions to handle noisy data and concept drift. These advancements are crucial for real-time applications such as human-agent collaboration, financial modeling, and adaptive control systems, where low-latency predictions and efficient data handling are paramount. The field is also actively exploring optimal regret bounds and the development of algorithms that adapt to unknown constraints or label shifts.