Resampling Method

Resampling methods are statistical techniques used to estimate the uncertainty of models and improve their performance, particularly in situations with limited data or non-standard distributions. Current research focuses on adapting resampling for high-dimensional data, addressing biases in non-standard settings (e.g., clustered or spatially correlated data), and developing efficient algorithms, such as those employing learned resampling functions or look-up tables, to speed up computation. These advancements are crucial for improving the reliability and efficiency of machine learning models across diverse applications, ranging from image processing and medical imaging to high-dimensional regression analysis.