WELCOME!

Weiwei Liu, Professor 

Email: liuweiwei863 AT gmail.com

Research Interests

My primary research interest is machine learning. Particularly, I focus on Robust Machine Learning, High Dimensional Analysis, Learning theory, etc.

Publications 

My Google Scholar Citations Page 

Conference Papers（* denotes the corresponding author ）

• 1. Zekai Wang, Zhengyu Zhou, Weiwei Liu*: DRF: Improving Certified Robustness via Distributional Robustness Framework. AAAI Conference on Artificial Intelligence (AAAI), 2024.

  2. Xin Zou, Weiwei Liu*: Coverage-guaranteed Prediction Sets for Out-of-distribution Data.AAAI Conference on Artificial Intelligence (AAAI), 2024.

  3. Lianghe Shi, Weiwei Liu*: A Closer Look at Curriculum Adversarial Training: From an Online Perspective. AAAI Conference on Artificial Intelligence (AAAI), 2024.

  4.  Weiwei Liu: Improved Bounds for Multi-task Learning with Trace Norm Regularization. Annual Conference on Learning Theory (COLT) 2023: 700-714.

  5. Xin Zou, Weiwei Liu*: On the Adversarial Robustness of Out-of-distribution Generalization Models. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2023.

  6. Jingyuan Xu, Weiwei Liu*: Characterization of Overfitting in Robust Multiclass Classification. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2023.

  7. Yanbo Chen, Weiwei Liu*: A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2023.

  8. Lianghe Shi, Weiwei Liu*: Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2023. 

  9. Chenglin Yu, Xinsong Ma, Weiwei Liu*: Delving into Noisy Label Detection with Clean Data. International Conference on Machine Learning (ICML) 2023: 40290-40305. (Oral). 

  10. Rui Gao, Weiwei Liu*: DDGR: Continual Learning with Deep Diffusion-based Generative Replay. International Conference on Machine Learning (ICML) 2023: 10744-10763.  

  11. Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu*,Shuicheng Yan: Better Diffusion Models Further Improve Adversarial Training. International Conference on Machine Learning (ICML) 2023: 36246-36263.

  12. Boqi Li, Weiwei Liu*: WAT: Improve the Worst-class Robustness in Adversarial Training. AAAI Conference on Artificial Intelligence (AAAI) 2023: 14982-14990.

  13. Xiaobo Shen, Yinfan Chen, Shirui Pan, Weiwei Liu, Yuhui Zheng: Graph Convolutional Incomplete Multi-modal Hashing. ACM International Conference on Multimedia (MM), 2023.

  14. Haobo Wang, Shisong Yang, Gengyu Lyu, Weiwei Liu, Tianlei Hu, Ke Chen, Songhe Feng, Gang Chen: Deep Partial Multi-Label Learning with Graph Disambiguation. International Joint Conference on Artificial Intelligence (IJCAI) 2023: 4308-4316.

  15. Xinsong Ma, Zekai Wang, Weiwei Liu*: On the Tradeoff Between Robustness and Fairness. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2022.  

  16. Xiyuan Li, Xin Zou, Weiwei Liu*: Defending Against Adversarial Attacks via Neural Dynamic System. Advances in Neural Information Processing Systems (NIPS/NeurIPS),2022.  

  17. Jingyuan Xu, Weiwei Liu*: On Robust Multiclass Learnability. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2022.

  18. Zekai Wang, Weiwei Liu*: Robustness Verification for Contrastive Learning. International Conference on Machine Learning (ICML), 2022: 22865-22883. (Long Presentation, 2% acceptance rate). 

  19. Yuren Mao, Zekai Wang, Weiwei Liu*, Xuemin Lin, Pengtao Xie: MetaWeighting: Learning to Weight Tasks in Multi-Task Learning. Annual Conference of the Association for Computational Linguistics (ACL) Findings, 2022: 3436-3448.

  20. Yuren Mao, Zekai Wang, Weiwei Liu*, Xuemin Lin, Wenbin Hu: BanditMTL: Bandit-based Multi-task Learning for Text Classification. Annual Conference of the Association for Computational Linguistics (ACL), 2021: 5506-5516.

  21. Yuren Mao, Weiwei Liu*, Xuemin Lin: Adaptive Adversarial Multi-task Representation Learning. International Conference on Machine Learning (ICML), 2020: 6724-6733.

  22. Yuren Mao, Shuang Yun, Weiwei Liu*, Bo Du: Tchebycheff Procedure for Multi-task Text Classification. Annual Conference of the Association for Computational Linguistics (ACL), 2020: 4217-4226.

  23. Xinjian Huang, Bo Du, Weiwei Liu*: Multichannel Color Image Denoising via Weighted Schatten p-norm Minimization. International Joint Conference on Artificial Intelligence (IJCAI), 2020: 637-644.

  24. Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu*: Opinion Maximization in Social Trust Networks. International Joint Conference on Artificial Intelligence (IJCAI), 2020: 1251-1257.

  25. Haobo Wang, Zhao Li, Jiaming Huang, Pengrui Hui,Weiwei Liu*, Tianlei Hu, Gang Chen: Collaboration Based Multi-Label Propagation for Fraud Detection. International Joint Conference on Artificial Intelligence (IJCAI), 2020: 2477-2483.

  26. Haobo Wang, Weiwei Liu*, Yang Zhao, Tianlei Hu, Ke Chen, Gang Chen: Learning From Multi-Dimensional Partial Labels. International Joint Conference on Artificial Intelligence (IJCAI), 2020: 2943-2949.  Code.

  27. Zhenyu Qiu, Wenbin Hu, Jia Wu, Weiwei Liu*, Bo Du, Xiaohua Jia: Temporal Network Embedding with High-Order Nonlinear Information. AAAI Conference on Artificial Intelligence (AAAI), 2020: 5436-5443.

  28. Haobo Wang, Chen Chen, Weiwei Liu, Ke Chen, Tianlei Hu, Gang Chen: Incorporating Label Embedding and Feature Augmentation for Multi-Dimensional Classification. AAAI Conference on Artificial Intelligence (AAAI), 2020: 6178-6185.

  29. Weiwei Liu: Copula Multi-label Learning. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2019: 6334-6343.

  30. Pinghua Xu, Wenbin Hu, Jia Wu,Weiwei Liu, Bo Du, Jian Yang: Social Trust Network Embedding. IEEE International Conference on Data Mining (ICDM), 2019: 678-687.

  31. Haobo Wang, Weiwei Liu, Yang Zhao, Chen Zhang, Tianlei Hu: Discriminative and Correlative Partial Multi-Label Learning. International Joint Conference on Artificial Intelligence (IJCAI), 2019: 3691-3697.

  32. Weiwei Liu, Xiaobo Shen: Sparse Extreme Multi-label Learning with Oracle Property. International Conference on Machine Learning (ICML), 2019: 4032-4041.

  33. Chen Chen, Haobo Wang, Weiwei Liu, Xingyuan Zhao, Tianlei Hu, Gang Chen: Two-Stage Label Embedding via Neural Factorization Machine for Multi-Label Classification. AAAI Conference on Artificial Intelligence (AAAI), 2019: 3304-3311.

  34. Xiaobo Shen, Weiwei Liu*, Yong Luo, Yew Soon Ong, Ivor W.Tsang: Deep Binary Prototype Multi-label Learning. International Joint Conference on Artificial Intelligence (IJCAI), 2018: 2675-2681.

  35. Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew Soon Ong, Quan-Sen Sun: Discrete Network Embedding. International Joint Conference on Artificial Intelligence (IJCAI), 2018: 3549-3555.

  36. Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi: Ranking Preserving Nonnegative Matrix Factorization. International Joint Conference on Artificial Intelligence (IJCAI), 2018: 2776-2782.

  37. Weiwei Liu, Zhuanghua Liu, Ivor W.Tsang, Wenjie Zhang, Xuemin Lin: Doubly Approximate Nearest Neighbor Classification. AAAI Conference on Artificial Intelligence (AAAI), 2018: 3683-3690. Slides.

  38. Xiaobo Shen, Weiwei Liu, Ivor W.Tsang, Quan-Sen Sun, Yew Soon Ong: Compact Multi-label Learning. AAAI Conference on Artificial Intelligence (AAAI), 2018: 4066-4073.

  39. Weiwei Liu, Xiaobo Shen, Ivor W.Tsang: Sparse Embedded k-Means Clustering. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2017: 3321-3329. Code. Slides.

  40. Jing Chai, Weiwei Liu,Ivor W.Tsang, Xiaobo Shen: Compact Multiple-Instance Learning. International Conference on Information and Knowledge Management (CIKM), 2017: 2007-2010.

  41. Xiaobo Shen, Weiwei Liu, Ivor W.Tsang, Fumin Shen, Quan-Sen Sun: Compressed K-means for Large-scale Clustering. AAAI Conference on Artificial Intelligence (AAAI), 2017: 2527-2533.

  42. Weiwei Liu, Ivor W.Tsang: Sparse Perceptron Decision Tree for Millions of Dimensions. AAAI Conference on Artificial Intelligence (AAAI), 2016: 1881-1887. Code. Slides.

  43. Weiwei Liu, Ivor W.Tsang: On the Optimality of Classifier Chain for Multi-label Classification. Advances in Neural Information Processing Systems (NIPS/NeurIPS), 2015: 712-720. Code. Slides.

  44. Weiwei Liu, Ivor W.Tsang: Large Margin Metric Learning for Multi-Label Prediction. AAAI Conference on Artificial Intelligence (AAAI), 2015: 2800-2806.  Code. Slides.

  45. Weiwei Liu, Zhi-Hong Deng, Xiuwen Gong, Frank Jiang, Ivor W. Tsang: Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network. AAAI Conference on Artificial Intelligence (AAAI), 2015: 210-216. Slides.

Journal Papers （* denotes the corresponding author ）

• 1. Zekai Wang,  Weiwei Liu*: RVCL: Evaluating the Robustness of Contrastive Learning via Verification . Journal of Machine Learning Research (JMLR), 2023. 

  2. Zhengyu Zhou,  Weiwei Liu*: Sample Complexity for Distributionally Robust Learning under $\chi^2$-divergence . Journal of Machine Learning Research (JMLR), 2023. 

  3. Xin Zou,  Weiwei Liu*: Generalization Bounds for Adversarial Contrastive Learning. Journal of Machine Learning Research (JMLR), 24: 114:1-114:54, 2023.

  4. Xinjian Huang, Weiwei Liu*, Bo Du, Dacheng Tao: Leveraged Matrix Completion with Noise. IEEE Transactions on Cybernetics (TCYB), 2023.

  5. Yuren Mao, Yu Hao, Weiwei Liu, Xuemin Lin, Xin Cao: Class-Imbalanced Aware Distantly Supervised Named Entity Recognition. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023.

  6. Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu, Yang Yang, Philip S. Yu: Signed Network Representation by Preserving Multi-Order Signed Proximity. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(3): 3087-3100, 2023. 

  7. Yuren Mao, Zekai Wang, Weiwei Liu*, Xuemin Lin, Wenbin Hu: Task Variance Regularized Multi-Task Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE), 35(8): 8615-8629, 2023.

  8. Xiaobo Shen, Yew-Soon Ong, Zheng Mao, Shirui Pan, Weiwei Liu, Yuhui Zheng: Compact network embedding for fast node classification. Pattern Recognition (PR), 136: 109236, 2023.

  9. Pinghua Xu, Wenbin Hu, Jia Wu, and Weiwei Liu: Signed Network Representation with Novel Node Proximity Evaluation. Neural Networks, 148: 142-154, 2022.

  10. Weiwei Liu, Haobo Wang, Xiaobo Shen, Ivor W.Tsang: The Emerging Trends of Multi-label Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 44(11): 7955-7974, 2022. 

  11. Chuang Liu, Jia Wu, Weiwei Liu, Wenbin Hu: Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information. Neural Networks, 142: 20-33, 2021.

  12. Weiwei Liu, Donna Xu, Ivor W.Tsang, Wenjie Zhang: Metric Learning for Multi-output Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(2): 408-422, 2019. Code.

  13. Weiwei Liu, Xiaobo Shen, Bo Du, Ivor W.Tsang, Wenjie Zhang, Xuemin Lin: Hyperspectral Imagery Classification via Stochastic HHSVMs. IEEE Transactions on Image Processing (TIP) 28(2): 577-588, 2019.

  14. Xiaobo Shen, Fumin Shen, Li Liu, Yun-Hao Yuan, Weiwei Liu, Quan-Sen Sun: Multi-view Discrete Hashing for Scalable Multimedia Search. ACM Transactions on Intelligent Systems and Technology (TIST), 9(5): 53:1-53:21, 2018. 

  15. Xiaobo Shen, Weiwei Liu, Ivor W.Tsang, Quan-Sen Sun, Yew-Soon Ong: Multi-label Prediction via Cross-view Search. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 29(9): 4324-4338, 2018.

  16. Weiwei Liu, Ivor W.Tsang: Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions. Journal of Machine Learning Research (JMLR), 18: 81:1-81:36, 2017. Code.

  17. Weiwei Liu, Ivor W. Tsang and Klaus-Robert Müller: An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels. Journal of Machine Learning Research (JMLR), 18: 94:1-94:38, 2017. Code.

  18.  Weiwei Liu, Zhi-Hong Deng, Xiaoran Xu, He Liu, Xiuwen Gong: Mining Top K Spread Sources for a Specific Topic and a Given Node. IEEE Transactions on Cybernetics (TCYB), 45(11): 2472-2483, 2015. 

PhD Thesis：Advanced Topics in Multi-label Learning

Research Grants

National Key R&D Program of China:

• 7,000,000 CNY total for four years. Co-Principal Investigator. ''The Health Risk Assessment, Prevention, and Product Development for Elderly and Children Community'' Jan. 2024 - Dec. 2027.

National Natural Science Foundation of China:

• 610,000 CNY total for four years. Sole Principal Investigator. ''The Research on The Theory of Large-scale Statistical Multi-label Learning.'' Jan. 2020 - Dec. 2023.

Science and Technology Major Project of Hubei Province (Next-Generation AI Technologies):

• 10,000,000 CNY total for three years. Co-Principal Investigator. ''Smart Medical Services Driven by Health Big Data.'' Oct. 2019 - Oct. 2022

The Fundamental Research Funds for the Central Universities:

• 3,000,000 CNY total for four years. Sole Principal Investigator. ''Large-scale Multi-label Learning.'' Jan. 2019 - Jan. 2023.

Students

Current Students:

• Xinsong Ma, PhD student, 2020-, (NeurIPS*1, ICML*1)

• Xiyuan Li, PhD student, 2020-, (NeurIPS*1)

• Rui Gao, PhD student, 2020-, (ICML*1)

• Zhenyu Zhou, PhD student, 2021-, (JMLR*1, AAAI*1)

• Xin Zou, PhD student, 2021-, (JMLR*1, NeurIPS*2, AAAI*1)

• Yanbo Chen, PhD student, 2022-, (NeurIPS*1)

•  Boqi Li, PhD student, 2023-, (AAAI*1)

• Jingyuan Xu, PhD student, 2023-, (NeurIPS*2)

• Chenglin Yu, Master student, 2020-, (ICML*1)

• Lianghe Shi, Master student, 2021-, (NeurIPS*1, AAAI*1)

• Zekai Wang, Master student, 2021-, (JMLR*1, ICML*2, NeurIPS*1, ACL*2, TKDE*1, AAAI*1)

• Qinghui Zeng, Master student, 2023-,

• Youjun Wang, Master student, 2023-,

• Yang Cao, Master student, 2023-,

Former Students:

• Yuren Mao, PhD student (co-supervised with Xuemin Lin), 2018-2021, now Assistant Professor at Zhejiang University

• Haobo Wang, PhD student (co-supervised with Gang Chen and Tianlei Hu), 2018-2023, now Assistant Professor at Zhejiang University

• Xinjian Huang, PhD student (co-supervised with Bo Du), 2018-2021, now Assistant Professor at Nanjing University of Science and Technology

• Shuang Yun, Master student (co-supervised with Bo Du), 2019-2021, now Alibaba Group

Professional Activity

Associate Editors:

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2024- .

Editorial Board Members:

• ACM Transactions on Probabilistic Machine Learning (TOPML), 2023-.

• Health Data Science, 2021-.

Leading Journal Guest Editor:

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS) Special Issue on Structured Multi-output Learning: Modelling, Algorithm, Theory and Applications, 2019-2020.

Conference Chair:

•  International Conference on Big Data and Computing (WBDC): 2023.

Conference Area Chair:

•  Advances in Neural Information Processing Systems (NIPS/NeurIPS): 2022, 2023.

•  The International Conference on Machine Learning (ICML): 2022.

•  International Conference on Learning Representations (ICLR): 2020, 2021, 2023, 2024.

Conference Senior Program Committee:

• International Joint Conference on Artificial Intelligence (IJCAI): 2020, 2021.

• Association for the Advancement of Artificial Intelligence (AAAI): 2021, 2022, 2023, 2024.

• Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD): 2019, 2020, 2021.

Conference Workshop Program Committee Co-Chair:

• The 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)Workshop on Multi-output Learning.

• The 10th Asian Conference on Machine Learning (ACML 2018) Workshop on Multi-output Learning.

Journal Reviewer:

• Journal of Machine Learning Research (JMLR).

• IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

• Artificial Intelligence Journal (AIJ).

• IEEE Transactions on Information Theory (TIT).

• IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

• IEEE Transactions on Cybernetics (TCYB). 

• IEEE Transactions on Signal Processing (TSP).

• IEEE Transactions on Knowledge and Data Engineering (TKDE).

• ACM Transactions on Knowledge Discovery from Data (TKDD).

• Machine Learning

• Pattern Recognition (PR).

• Pattern Recognition Letters.

• Neural Networks

• IET Computer Vision

Conference Program Committee:

• Association for the Advancement of Artificial Intelligence (AAAI): 2018, 2019, 2020.

• International Joint Conference on Artificial Intelligence (IJCAI): 2015, 2018, 2019.

• The Conference on Uncertainty in Artificial Intelligence (UAI): 2018.

• IEEE International Conference on Data Engineering (ICDE): 2019, 2020.

• European Conference on Computer Vision (ECCV): 2018.

• Asian Conference on Machine Learning (ACML): 2017, 2018, 2019.

• The International World Wide Web Conference (WWW): 2019, 2020.

• SIAM International Conference on Data Mining (SDM): 2019, 2020.

• ACM International Conference on Information and Knowledge Management (CIKM): 2019.

• IEEE International Conference on Data Mining (ICDM): 2019.

Conference Reviewer:

• Advances in Neural Information Processing Systems (NIPS): 2015, 2016, 2019, 2020, 2021.

• The International Conference on Machine Learning (ICML): 2019, 2020, 2021.

• The International Conference on Algorithmic Learning Theory (ALT): 2024.

• International Conference on Artificial Intelligence and Statistics (AISTATS): 2019, 2020.

• IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR): 2019, 2020.

• IEEE International Conference on Computer Vision (ICCV): 2019.

Teaching Experience

• 2022 Fall: Introduction to Internet of Things, Wuhan University

• 2021 Fall: Introduction to Internet of Things, Wuhan University

• 2020 Fall: Introduction to Internet of Things, Wuhan University

• 2019 Fall: Introduction to Internet of Things, Wuhan University.

Awards

• World Artificial Intelligence Conference Youth Outstanding Paper Award

• IEEE Senior Member

• 2017 Chinese Government Award for Outstanding Self-financed Students Abroad

• Outstanding Reviewer Award of Pattern Recognition Journal

• Best Theory Paper Award from Centre for Artificial Intelligence, University of Technology Sydney

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