Data Generating

Data generation research focuses on understanding and modeling the processes that produce datasets, aiming to improve the accuracy and robustness of machine learning models. Current research explores alternative frameworks beyond traditional probability distributions, such as finite population models, and investigates how to characterize generalization performance using worst-case probability measures. Significant attention is given to handling concept drift (changes in the data-generating distribution over time), particularly in streaming data contexts, and to developing generative models like diffusion models that accurately capture the underlying data manifold. These advancements are crucial for building more reliable and adaptable machine learning systems across diverse applications.