Dynamic Screening

Dynamic screening is a set of techniques designed to accelerate machine learning model training by efficiently identifying and discarding irrelevant data points or features before or during the learning process. Current research focuses on developing more robust screening methods, particularly those that are resilient to variations in data distribution (distributionally robust safe screening) and adaptable to distributed computing environments (distributed dynamic safe screening). These advancements significantly improve the efficiency of training large-scale models, impacting fields like medical image analysis and natural language processing by reducing computational costs and enabling the analysis of larger datasets.