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.
I am engaged in machine learning research. Research Interests
I have strong interests in weakly supervised learning (Incomplete/Inaccurate/Inexact Supervised Learning)and efficient optimization algorithm.
My current foucues are
- Semi-Supervised Leanring
- Label Noise Learning
- Bi-Level Optimization
- B.Sc – Software Institute, Jilin University, 2013 - 2017.
- PhD – LAMDA Group, Computer Science & Techonology, Nanjing University, 2017 - Now
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.
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.
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.
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.
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.
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.
- Digital Image Processing, in Nanjing University, China, 2017-2018 Fall.
- Introduction to Machine Learning, in Nanjing University, China, 2018-2019 Spring.
- Programming: C/C++, Python, MATLAB, Sql, Spark.
- Machine Learning: Tensorflow, Pytorch, MXNet, Sklearn.
Reading Notes:Deep SSL-Pseudo-Label (ICML 2013).
Reading Notes:Deep SSL-Ladder Network (NIPS 2015).
Reading Notes:Deep SSL-MixMatch & UDA.
Methods of Data Augmentation.
How to Optimize AUC.
Reading Notes:Semi-Supervised Classification with Graph Convolutional Networks.