Shi-Jiang Li

SIAGXC: a prediction-augmented graph model on extreme multi-label classification

SIAGXC, a prediction-augmented graph framework for XMLC that leverages auxiliary relational signals derived from an upstream XMLC model, demonstrates that prediction-derived auxiliary relations provide an effective way to enhance graph-based XMLC.