Enhancing Binary Relevance for Multi-label Learning with Controlled Label Correlations Exploitation. - Yu-Kun Li; Min-Ling Zhang (PRICAI, 2014)

Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning. - Yu-Kun Li; Min-Ling Zhang; Xin Geng 001 (ICDM, 2015)

Towards Class-Imbalance Aware Multi-Label Learning. - Min-Ling Zhang; Yu-Kun Li; Xu-Ying Liu (IJCAI, 2015)

Binary relevance for multi-label learning: an overview. - Min-Ling Zhang; Yu-Kun Li; Xu-Ying Liu; Xin Geng 001 (Frontiers Comput. Sci., 2018)

ERNIE: Enhanced Representation through Knowledge Integration. - Yu Sun 004; Shuohuan Wang; Yu-Kun Li; Shikun Feng; Xuyi Chen; Han Zhang; Xin Tian 011; Danxiang Zhu; Hao Tian 005; Hua Wu 003 (CoRR, 2019)

ERNIE 2.0: A Continual Pre-Training Framework for Language Understanding. - Yu Sun 004; Shuohuan Wang; Yu-Kun Li; Shikun Feng; Hao Tian 005; Hua Wu 003; Haifeng Wang 001 (AAAI, 2020)

ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation. - Dongling Xiao; Han Zhang; Yu-Kun Li; Yu Sun 004; Hao Tian 005; Hua Wu 003; Haifeng Wang 001 (IJCAI, 2020)

ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding. - Dongling Xiao; Yu-Kun Li; Han Zhang; Yu Sun 029; Hao Tian 005; Hua Wu 003; Haifeng Wang 001 (CoRR, 2020)

ERNIE-Gram: Pre-Training with Explicitly N-Gram Masked Language Modeling for Natural Language Understanding. - Dongling Xiao; Yu-Kun Li; Han Zhang; Yu Sun 004; Hao Tian 005; Hua Wu 003; Haifeng Wang 001 (NAACL-HLT, 2021)

Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning. - Min-Ling Zhang; Qian-Wen Zhang; Jun-Peng Fang; Yu-Kun Li; Xin Geng 001 (IEEE Trans. Knowl. Data Eng., 2021)

Towards Class-Imbalance Aware Multi-Label Learning. - Min-Ling Zhang; Yu-Kun Li; Hao Yang 015; Xu-Ying Liu (IEEE Trans. Cybern., 2022)