Dr. Guoqiang Wu is an associate researcher in the School of Software, Shandong University. He received his bachelor's degree from Shandong University in 2012. Then, he received his master's and Ph.D. degrees from the University of Chinese Academy of Sciences in 2015 and 2019, respectively. From 2019 to 2021, he did his postdoc research at Tsinghua University. His research interests mainly concentrate on machine learning, optimization, artificial intelligence, especially statistical learning theory. He has published several papers in some reputable international conferences and journals on machine learning, such as ICML, NeurIPS, Neural Networks. He is (or was) the reviewer of many reputable journals (e.g., TPAMI) and conferences (e.g., ICML, NeurIPS, ICLR).
News:
2023/4/25. Two papers are accepted in ICML 2023. Congrats and thanks to all my co-authors.
2021/9/29. Three papers are accepted in NeurIPS 2021. Thanks to all my co-authors.
Guoqiang Wu, Chongxuan Li, Yilong Yin. Towards Understanding Generalization of Macro-AUC in Multi-label Learning,
International Conference on Machine Learning (ICML) 2023
Chenyu Zheng, Guoqiang Wu, Fan Bao, Yue Cao, Chongxuan Li, Jun Zhu. Revisiting Discriminative vs. Generative Classifiers: Theory and Implications,
International Conference on Machine Learning (ICML) 2023
Guoqiang Wu*, Chongxuan Li*, Kun Xu, and Jun Zhu. Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization, Advances in Neural Information Processing Systems (NeurIPS) 2021
Fan Bao*, Guoqiang Wu*, Chongxuan Li*, Jun Zhu, and Bo Zhang. Stability and Generalization of Bilevel Programming in Hyperparameter Optimization, Advances in Neural Information Processing Systems (NeurIPS) 2021
Shuyu Cheng, Guoqiang Wu, and Jun Zhu. On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms, Advances in Neural Information Processing Systems (NeurIPS) 2021
Guoqiang Wu, and Jun Zhu. Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? Advances in Neural Information Processing Systems (NeurIPS) 2020
Guoqiang Wu, Ruobing Zheng, Yingjie Tian and Dalian Liu. Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification, Neural Networks 2020
Guoqiang Wu, Yingjie Tian, and Dalian Liu. Cost-sensitive multi-label learning with positive and negative label pairwise correlations, Neural Networks 2018
Guoqiang Wu, Yingjie Tian, and Chunhua Zhang. A unified framework implementing linear binary relevance for multi-label learning, Neurocomputing 2018
Guoqiang Wu, Yingjie Tian, and Dalian Liu. Privileged Multi-Target Support Vector Regression, International Conference on Pattern Recognition (ICPR) 2018
