Complement Submodular Information Measures for Balanced and Robust Data Selection

ArXi:2605.24779v1 Announce Type: cross Submodular optimization has become a fundamental paradigm for data selection, retrieval, summarization, and representation learning due to its ability to model coverage, diversity, and representativeness. However, classical submodular objectives optimize only the selected subset and do not explicitly preserve structural information between the selected subset and the remaining data.