Confidence Band

Confidence bands provide a visual representation of uncertainty surrounding estimated functions or curves, offering a more comprehensive assessment than point estimates alone. Current research focuses on developing robust and valid confidence bands for diverse applications, including deep learning models, time series analysis, and hyperparameter tuning in machine learning, employing techniques like bootstrapping, kernel methods, and optimization algorithms to achieve non-asymptotic guarantees. These advancements are crucial for reliable inference and decision-making in various scientific fields and practical applications where understanding prediction uncertainty is paramount. The development of efficient and accurate methods for constructing confidence bands continues to be a significant area of investigation.