Distributionally Robust Safe Screening: Identifying Unnecessary Samples and Features in Dynamically Changing Environments

The proposed Distributionally Robust Safe Screening (DRSS) method can reliably identify unnecessary samples and features in supervised learning problems, even when the data distribution changes within a specified range during the test phase.