Weizhong Zhang
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Tenure-track Professor School of Data Science Contactweizhongzhang@fudan.edu.cn |
About Me
I am now a Tenure-track Professor at School of Data Science, Fudan University. I was a Postdoctoral Researcher and Research Assistant Professor in The Hong Kong University of Science and Technology, working with Prof. Tong Zhang. I was also a Research Scientist in Tencent AI Lab. I received my Ph.D. on Computer Science at Zhejiang University under my supervisors Prof. Xaiofei He and Prof. Deng Cai. I was a Joint PhD student in The University of Michigan, Ann Arbor under the supervision of Prof. Jieping Ye and Prof. Jie Wang.
My current research interest is Machine Learning, Sparse Neural Networks, Efficient Training/Inference, Federated Learning, OOD generalziation, etc.
Education
2012.09-2017.06 Ph.D. candidate in Computer Science, Zhejiang University
2015.10-2016.10 Joint Ph.D. student in University of Michigan, Ann Arbor
2008.09-2012.06 B.S. in Mathematics and Applied Mathematics, Zhejiang University
Publications (*equal contribution)
1. Weizhong Zhang*, Yong Lin*, Xiao Zhou*, Tong Zhang. Sparse Invariant Risk Minimization. In International Conference on Machine Learning, pp. 27222-27244, 2022. (ICML 2022)
2. Weizhong Zhang*, Xiao Zhou*, Renjie Pi*, Yong Lin, Tong Zhang. Probabilistic Bilevel Coreset Selection. In International Conference on Machine Learning, pp. 27287-27302, 2022. (ICML 2022)
3. Xupeng Shi, Pengfei Zheng, A. Adam Ding, Yuan Gao, Weizhong Zhang. Finding Dynamics Preserving Adversarial Winning Tickets. In International Conference on Artificial Intelligence and Statistics, pp. 510-528, 2022. (AISTATS 2022)
4. Cong Fang, Yihong Gu, Weizhong Zhang, and Tong Zhang. Convex Formulation of Overparameterized Deep Neural Networks. IEEE Transactions on Information Theory,68(8): 5340-5352, 2022(IEEE TIT)
5. Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang. Model Agnostic Sample Reweighting for Out-of-Distribution Learning. In International Conference on Machine Learning, pp. 27203-27221, 2022. (ICML 2022)
6. Tingting Liang, Lin Ma, Weizhong Zhang, Haoran Xu, Congying Xia, and Yuyu Yin. Content-aware recommendation via Dynamic Heterogeneous Graph Convolutional Network. Knowledge-Based Systems (2022): 109185.
7. Weizhong Zhang*, Xiao Zhou*, Hang Xu, Tong Zhang, Effective Sparsification of Neural Networks with Global Sparsity Constraint. IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3599-3608, 2021. (CVPR 2021)
8. Weizhong Zhang*, Xiao Zhou*, Zonghao Chen, Shizhe Diao, Tong Zhang, Efficient Neural Network Training via Forward and Backward Propagation Sparsification. In proceedings of the 32nd Annual Conference on Neural Information Processing Systems 34(2021). (Neurips 2021)
9. Weizhong Zhang*, Yihong Gu*, Cong Fang, Jason Lee, Tong Zhang, How to Characterize The Landscape of Overparameterized Convolutional Neural Networks. In proceedings of the 32nd Annual Conference on Neural Information Processing Systems,33 (2020): 3797-3807. (NeurIPS 2020)
10. Weizhong Zhang*, Bin Hong*, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang, Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction. Journal of Machine Learning Research, 20 (2019): 121-1. (JMLR 2019)
11. Fangyu Zou, Li Shen, Zequn Jie, Weizhong Zhang, Wei Liu, A Sufficient Condition for Convergences of Adam and RMSProp. IEEE Conference on Computer Vision and Pattern Recognition, pp. 11127-11135, 2019. (CVPR 2019)
12. Weizhong Zhang, Tingjin Luo, Shuang Qiu, Jieping Ye, Dengcai, Xiaofei He, Jie Wang, Identifying Genetic Risk Factors for Alzheimer's Disease via Shared Tree-guided Feature Learning across Multiple Tasks. IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 11, 2018. (TKDE 2018)
13. Weizhong Zhang, Bin Hong, Lin Ma, Wei Liu, Tong Zhang, Safe Element Screening for Submodular Function Minimization. In Proceedings of the 35th International Conference on Machine Learning, pp. 5786-5795, 2018. (ICML 2018)
14. Xing Yan, Weizhong Zhang, Lin Ma, Wei Liu, and Qi Wu, Parsimonious Quantile Regression of Asymmetrically Heavy-tailed Financial Return Series. In proceedings of the 32nd Annual Conference on Neural Information Processing Systems, 31(2018). (NeurIPS 2018)
15. Weizhong Zhang, Lijun Zhang, Zhongming Jin, Rong Jin, Deng Cai, Xuelong Li, Ronghua Liang, Xiaofei He: Sparse Learning with Stochastic Composite Optimization. IEEE Transduction on Pattern Analysis and Machine Intelligence, 39(6):1223-1236, 2017. (TPAMI 2017)
16. Weizhong Zhang*, Bin Hong*, Jieping Ye, Deng Cai, Xiaofei He, Jie Wang, Scaling Up Sparse Support Vector Machine by Simultaneous Feature and Sample Reduction. In Proceedings of the 34th International Conference on Machine Learning, pp. 4016-4025, 2017. (ICML 2017)
17. Tingjin Luo, Weizhong Zhang, Shuang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye, Jie Wang, Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning. In Proceedings of the 23st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 345-354, 2017. (KDD 2017)
18. Weizhong Zhang, Lijun Zhang, Rong Jin, Deng Cai, Xiaofei He: Accelerated Sparse Linear Regression via Random Projection. In Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 2337-2343, 2016. (AAAI 2016)
19. Weizhong Zhang, Lijun Zhang, Yao Hu, Rong Jin, Deng Cai, Xiaofei He: Sparse Learning for Stochastic Composite Optimization. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 893-899, 2014. (AAAI 2014）
Teaching
Spring 2023 Multivariate Statistics (MATH 620156)
Summer 2022 Applied Statistics (MATH 2411)
Fall 2021 Calculus and Linear Algebra (MATH 1003)
Fall 2020 Calculus and Linear Algebra (MATH 1003)