Distributionally Robust Safe Screening

arXiv:2404.16328v1 Announce Type: cross
Abstract: In this study, we propose a method Distributionally Robust Safe Screening (DRSS), for identifying unnecessary samples and features within a DR covariate shift setting. This method effectively combines DR learning, a paradigm aimed at enhancing model robustness against variations in data distribution, with safe screening (SS), a sparse optimization technique designed to identify irrelevant samples and features prior to model training. The core concept of the DRSS method involves reformulating the DR covariate-shift problem as …

abstract arxiv cs.lg data distribution features model robustness optimization paradigm robust robustness safe samples screening shift stat.ml study type