Subsampling Method

Subsampling methods aim to reduce computational cost and improve efficiency in various machine learning and data analysis tasks by selectively using subsets of data. Current research focuses on developing optimal subsampling strategies for different applications, including deep neural networks, differential privacy mechanisms, and time series analysis, often employing techniques like stochastic average pooling, model-based subsampling, and phylogeny-informed subsampling. These advancements are significant because they enable the application of complex algorithms to massive datasets, improve the privacy and robustness of machine learning models, and enhance the efficiency of various computational processes.