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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 (SUFE).

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

My research interests broadly lie in the foundations of machine learning, including architecture, optimization, generalization, generative modeling, and representation learning.

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

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Publications

SUFE:

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

• Jianlong Chen, Zhiming Zhou.
• ICLR 2026.

Learning Personalizable Clustered Embedding for Recommender Systems. [pdf]

• Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou.
• TORS 2025.

Residual Multi-Task Learner for Applied Ranking. [pdf]

• Cong Fu, Kun Wang, Jiahua Wu, Yizhou Chen, Guangda Huzhang, Yabo Ni, Anxiang Zeng, Zhiming Zhou.
• KDD 2024.

Recurrent Temporal Revision Graph Networks. [pdf]

• Yizhou Chen, Anxiang Zeng, Guangda Huzhang, Qingtao Yu, Kerui Zhang, Cao Yuanpeng, Kangle Wu, Han Yu, Zhiming Zhou.
• NeurIPS 2023.

Clustered Embedding Learning for Recommender Systems. [pdf]

• Yizhou Chen, Guangda Huzhang, Anxiang Zeng, Qingtao Yu, Hui Sun, Heng-Yi Li, Jingyi Li, Yabo Ni, Han Yu, Zhiming Zhou.
• WWW 2023.

SJTU:

Lipschitz Generative Adversarial Nets. [pdf]

• Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Weinan Zhang, Yong Yu, Zhihua Zhang.
• ICML 2019.

AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods. [pdf]

• Zhiming Zhou*, Qingru Zhang*, Guansong Lu, Hongwei Wang, Weinan Zhang, Yong Yu.
• ICLR 2019.

Guiding the One-to-one Mapping in CycleGAN via Optimal Transport. [pdf]

• Guansong Lu, Zhiming Zhou, Yuxuan Song, Kan Ren, Yong Yu.
• AAAI 2019.

Triple-to-Text: Converting RDF Triples into High-Quality Natural Languages via Optimizing an Inverse KL Divergence. [pdf]

• Yaoming Zhu, Juncheng Wan, Zhiming Zhou, Liheng Chen, Lin Qiu, Weinan Zhang, Xin Jiang, Yong Yu.
• SIGIR 2019.

Activation Maximization Generative Adversarial Nets. [pdf]

• Zhiming Zhou, Han Cai, Shunlin Rong, Yuxuan Song, Kan Ren, Weinan Zhang, Jun Wang, Yong Yu.
• ICLR 2018.

Learning to Design Games: Strategic Environments in Deep Reinforcement Learning. [pdf]

• Haifeng Zhang, Jun Wang, Zhiming Zhou, Weinan Zhang, Ying Wen, Yong Yu, Wenxin Li
• IJCAI 2018.

Unsupervised Diverse Colorization via Generative Adversarial Networks. [pdf]

• Yun Cao, Zhiming Zhou, Weinan Zhang, Yong Yu.
• ECML 2017.

Sparse-as-Possible SVBRDF Acquisition. [pdf]

• Zhiming Zhou, Guojun Chen, Yue Dong, David Wipf, Yong Yu, John Snyder, Xin Tong.
• TOG 2016.