Lan-Zhe Guo


E-mail : Wechat: z2546191786




I am a PhD student of Department of Computer Science & Technology in Nanjing University and a member of LAMDA Group, which is lead by professor Zhi-Hua Zhou. My supervisor is Yu-Feng Li. Before that, I received my B.Sc. degree from Software College of Jilin University.

Research Interests

I am engaged in machine learning research.
I have strong interests in weakly supervised learning (Incomplete/Inaccurate/Inexact Supervised Learning)and efficient optimization algorithm.
My current foucues are



  1. Lan-Zhe Guo, Yu-Feng Li. A General Formulation for Safely Exploiting Weakly Supervised Data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18), New Orleans, LA.
  2. Lan-Zhe Guo, Shao-Bo Wang, Yu-Feng Li. Large Margin Graph Construction for Semi-Supervised Learning. In: 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018: 1030-1033.
  3. Lan-Zhe Guo, Tao Han, Yu-Feng Li. Robust Semi-Supervised Representation Learning for Graph-Structured Data. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'19). Macau, China. 2019.
  4. Lan-Zhe Guo, Yu-Feng Li, Ming Li, Jin-Feng Yi, Bo-Wen Zhou, Zhi-Hua Zhou. Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness. arXiv preprint arXiv:1904.09743.
  5. Tong Wei, Lan-Zhe Guo, Yu-Feng Li, Wei Gao. Learning Safe Multi-Label Prediction for Weakly Labeled Data. Machine Learning, 2018, 107(4): 703-725.
  6. Yu-Feng Li, Lan-Zhe Guo (co-first author), Zhi-Hua Zhou. Towards Safe Weakly Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), in press.

Teaching Experiences


  1. Programming: C/C++, Python, MATLAB, Sql, Spark.
  2. Machine Learning: Tensorflow, Pytorch, MXNet, Sklearn.


  1. Reading Notes:Deep SSL-Pseudo-Label (ICML 2013).
  2. Reading Notes:Deep SSL-Ladder Network (NIPS 2015).
  3. Reading Notes:Deep SSL-MixMatch & UDA.
  4. Methods of Data Augmentation.
  5. Reading Notes:Semi-Supervised Classification with Graph Convolutional Networks.