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Biography

I am an Assistant Professor in the School of Computing and Artificial Intelligence at Shanghai University of Finance and Economics.

I obtained my Ph.D. in Computer Science from Shanghai Jiao Tong University in 2020 and my B.S. in Computer Science from the ACM Class at Shanghai Jiao Tong University in 2014.

I am broadly interested in fundamental techniques and theories of machine learning, including generative models, optimization, generalization, architecture, representation learning, learning theory, etc.

Ultimately, I aim to lay a solid foundation for artificial intelligence.

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Prospective Students

Selected Works

Revisiting Sharpness-Aware Minimization: A More Faithful and Effective Implementation. [openreview]

• Jianlong Chen, Zhiming Zhou.
• International Conference on Learning Representations (ICLR), 2026.

Residual Multi-Task Learner for Applied Ranking. [openreview][acm][slide]

• Cong Fu, Kun Wang, Jiahua Wu, Yizhou Chen, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Zhiming Zhou.
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024.

Recurrent Temporal Revision Graph Networks. [openreview][acm] [arxiv]

• Yizhou Chen, Anxiang Zeng, Guangda Huzhang, Qingtao Yu, Kerui Zhang, Cao Yuanpeng, Kangle Wu, Han Yu, Zhiming Zhou.
• Annual Conference on Neural Information Processing Systems (NeurIPS), 2023.

Clustered Embedding Learning for Recommender Systems. [acm] [arxiv]

• Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou.
• ACM Web Conference (WWW), 2023.

Lipschitz Generative Adversarial Nets. [pmlr] [arxiv] [slide] [code] [code]

• Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang.
• International Conference on Machine Learning (ICML), 2019.

AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods. [openreview] [arxiv] [poster] [code]

• Zhiming Zhou*, Qingru Zhang*, Guansong Lu, Hongwei Wang, Weinan Zhang, Yong Yu.
• International Conference on Learning Representations (ICLR), 2019.

Activation Maximization Generative Adversarial Nets. [openreview] [arxiv] [code] [code]

• Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu.
• International Conference on Learning Representations (ICLR), 2018.

Sparse-as-Possible SVBRDF Acquisition. [acm] [pdf] [slide]

• Zhiming Zhou, Guojun Chen, Yue Dong, David Wipf, Yong Yu, John Snyder, Xin Tong.
• ACM Transactions on Graphics (TOG) - ACM SIGGRAPH Asia, 2016.