Complement Submodular Information Measures for Balanced and Robust Data Selection 事件

Complement Submodular Information Measures for Balanced and Robust Data Selection arXiv:2605.24779v1 Announce Type: cross Abstract: 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 t