吴国强，副研究员，硕士生导师，泰山学者青年专家（免答辩），入选山东大学青年学者未来计划，2012年本科毕业于山东大学，2015年和2019年分别硕士和博士毕业于中国科学院大学，2019-2021年在清华大学从事博士后研究工作。主要从事机器学习、优化、人工智能等方面的研究工作，尤其是统计学习理论。在机器学习重要国际会议和期刊如ICML、NeurIPS、Neural Networks等发表过多篇论文。长期担任机器学习重要期刊（如TPAMI）以及重要会议（如机器学习三大顶级会议ICML、NeurIPS、ICLR）的审稿人。
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招生方向：
来源：软件工程、计算机科学、人工智能、数学、物理、自动化等
硕士生专业：软件工程；人工智能
欢迎对机器学习感兴趣的数学与英语能力强的同学与我联系。
软件工程：学硕（2024秋入学，剩余1个名额），专硕（2024秋入学，剩余1个名额）
人工智能：学硕（2024秋入学，剩余1个名额），专硕（2024秋入学，剩余1个名额）
欢迎2024级保研同学邮件与我联系。
研究方向：
机器学习
News:
2023/12/9. One paper is acceted in AAAI 2024. Congrats to Bingzheng, Teng Pang and Yan Zhang.
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 (CCF-A)
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 (CCF-A)
Chenyu Zheng, Guoqiang Wu, Chongxuan Li†. Toward Understanding Generative Data Augmentation, Advances in Neural Information Processing Systems (NeurIPS) 2023 (CCF-A)
Bingzheng Wang, Guoqiang Wu†, Teng Pang, Yan Zhang, Yilong Yin†. DiffAIL: Diffusion Adversarial Imitation Learning, AAAI Conference on Artificial Intelligence (AAAI) (CCF-A)
Guoqiang Wu, Jun Zhu†. Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning? Asian Conference on Machine Learning (ACML) 2023 (CCF-C)
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 (CCF-A)
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 (CCF-A)
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 (CCF-A)
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 (CCF-A)
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 (中科院SCI一区，CCF-B)
Guoqiang Wu, Yingjie Tian, and Dalian Liu. Cost-sensitive multi-label learning with positive and negative label pairwise correlations, Neural Networks 2018 (中科院SCI一区，CCF-B)
Guoqiang Wu, Yingjie Tian, and Chunhua Zhang. A unified framework implementing linear binary relevance for multi-label learning, Neurocomputing 2018 (中科院SCI二区，CCF-C)
Guoqiang Wu, Yingjie Tian, and Dalian Liu. Privileged Multi-Target Support Vector Regression, International Conference on Pattern Recognition (ICPR) 2018 (CCF-C)
