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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, representation learning, architecture, etc.

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

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Prospective Students

Publications

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.

Exposure Bias versus Self-Recovery: Are Distortions Really Incremental for Autoregressive Text Generation? [pdf]

• Tianxing He, Jingzhao Zhang, Zhiming Zhou, James Glass.
• EMNLP 2021.

Improving Unsupervised Domain Adaptation with Variational Information Bottleneck. [pdf]

• Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou, Jian Shen, Shuo Shao, Weinan Zhang, Yong Yu.
• ECAI 2020.

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.