Prediction Problem

Prediction problems, aiming to accurately forecast future outcomes based on available data, are a central focus in machine learning. Current research emphasizes robust prediction methods for diverse data types, including streaming data and out-of-distribution scenarios, employing techniques like gradient boosting, recurrent neural networks, and conformal prediction. Addressing challenges such as distribution shift and ensuring reliable uncertainty quantification are key priorities, with a focus on developing algorithms that guarantee performance and fairness across various contexts. These advancements have significant implications for numerous fields, improving the accuracy and reliability of predictions in applications ranging from financial forecasting to medical diagnosis.