🤵🏻 About Me
I am a machine learning researcher with research interests in weakly supervised learning, open environment machine learning, learnware. I obtained my Ph.D. degree from Department of Computer Science and Technology in Nanjing University in June 2022, where I was very fortunate to be advised by professor Yu-Feng Li (李宇峰). Currently, I am an Assistant Professor in School of Intelligence Science and Technology, Nanjing University (Suzhou Campus). I am also a member of LAMDA Group (机器学习与数据挖掘研究所), which is led by professor Zhi-Hua Zhou (周志华).
My research interests include topics in machine learning and data mining. Most recently, I am interested in the following topics:
- Weakly Supervised Learning
- Incomplete Supervision: Only a subset of training data is given with labels.
- Inexact Supervision: The training data is given with only coarse-grained labels.
- Inaccurate Supervision: The given labels are not always ground-truth.
- Robust Machine Learning in Open Environments
- Robust machine learning with unseen class or out-of-distribution (OOD) data.
- Robust machine learning with decremental/incremental features.
- Robust machine learning with changing data distributions.
- Learnware and Machine Learning Model Adaptation
- How to assign model a specification to describe its specialty and utility.
- How to connect various machine learning models via model specification.
- How to reuse/adapt pre-trained models to solve different AI tasks.
📢: 招收对机器学习、人工智能感兴趣,有较好编程基础的学生,可通过邮箱guolz@nju.edu.cn联系我,请附上个人简历、成绩单及自我介绍。
🎉 News
- 2023.06: One paper is accepted by ECML 2023!
- 2023.05: We open-sourced a LLM for Chinese Legal domain LawGPT.⛏️ 🛠️
- 2023.04: Three papers are accepted by ICML 2023!
- 2022.10: Two papers are accepted by Machine Learning!
- 2022.09: Three papers are accepted by NeurIPS 2022!
- 2022.07: We open-sourced the semi-supervised learning toolkit LAMDA-SSL.⛏️ 🛠️
- 2022.04: One paper is accepted by ICML 2022!
📝 Publications
Conference Papers
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Identifying Useful Learnwares for Heterogeneous Label Spaces.
Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li, Zhi-Hua Zhou.
In: Proceedings of the 40th International Conference on Machine Learning (ICML 2023), Hawaii, 2023. Page: 12122-12131.
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Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions.
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yu-Ke Xiang, Yu-Feng Li.
In: Proceedings of the 40th International Conference on Machine Learning (ICML 2023), Hawaii, 2023. Page: 14886-14901.
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ODS: Test-Time Adaptation in the Presence of Open-World Data Shift.
Zhi Zhou, Lan-Zhe Guo, Lin-Han Jia, Ding-Chu Zhang, Yu-Feng Li.
In: Proceedings of the 40th International Conference on Machine Learning (ICML 2023), Hawaii, 2023. Page: 42574-42588.
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Robust Semi-Supervised Learning when Not All Classes have Labels.
Lan-Zhe Guo, Yi-Ge Zhang, Zhi-Fan Wu, Jie-Jing Shao, Yu-Feng Li.
In: Advances in Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, 2022. Page: 3305-3317.
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LOG: Active Model Adaptation for Label-Efficient OOD Generalization.
Jie-Jing Shao, Lan-Zhe Guo, Xiao-Wen Yang, Yu-Feng Li.
In: Advances in Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, 2022. Page: 11023-11034.
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USB: A Unified Semi-Supervised Learning Benchmark
for Classification.
Yi-Dong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wen-Xin Hou, Ren-Jie Wang, Lin-Yi Yang, Zhi Zhou, Lan-Zhe Guo, He-Li Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jin-Dong Wang, Xing Xie, Yue Zhang.
In: Advances in Neural Information Processing Systems Datasets and Benchmarks (NeurIPS 2022 Datasets and Benchmarks), New Orleans, LA, 2022. Page: 3938-3961.
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Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding.
Lan-Zhe Guo, Yu-Feng Li.
In: Proceedings of the 39th International Conference on Machine Learning (ICML 2022), Baltimore, Maryland, 2022. Page: 8082-8094.
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STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data.
Zhi Zhou, Lan-Zhe Guo (co-first author), Zhan-Zhan Cheng, Yu-Feng Li, Shi-Liang Pu.
In: Advances in Neural Information Processing Systems (NeurIPS 2021), Virtual Conference, 2021. Page: 29168-29180.
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Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment.
Lan-Zhe Guo, Zhi Zhou, Jie-Jing Shao, Yu-Feng Li, and DiDi Collaborators.
In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), Singapore, 2021. Page: 487-495.
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Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data.
Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, Zhi-Hua Zhou.
In: Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria, 2020. Page: 3897-3906.
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RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift.
Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li.
In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), San Diego, CA, 2020. Page: 1636-1644.
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Weakly Supervised Learning Meets Ride-Sharing User Experience Enhancement.
Lan-Zhe Guo, Feng Kuang, Zhang-Xun Liu, Yu-Feng Li, Nan Ma, Xiao-Hu Qie.
In: Proceedings of the 34rd AAAI conference on Artificial Intelligence (AAAI 2020), New York, NY, 2020. Page: 4052-4059.
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Robust Semi-Supervised Representation Learning for Graph-Structured Data.
Lan-Zhe Guo, Tao Han, Yu-Feng Li.
In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019), Macau, China. 2019. Page: 131--143.
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A General Formulation for Safely Exploiting Weakly Supervised Data.
Lan-Zhe Guo, Yu-Feng Li.
In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI 2018), New Orleans, LA, 2018. Page: 3126-3133.
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Large Margin Graph Construction for Semi-Supervised Learning.
Lan-Zhe Guo, Shao-Bo Wang, Yu-Feng Li.
In: 2018 IEEE International Conference on Data Mining Workshops (ICDM 2018), Singapore, 2018. Page: 1030-1033.
Journal Papers
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LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit.
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li.
SCIENCE CHINA Information Sciences, in press, 2023. -
稳健选择伪标注的混合式半监督学习.
Lan-Zhe Guo, Yu-Feng Li.
中国科学:信息科学, in press, 2023. -
Open-Set Learning under Covariate Shift.
Jie-Jing Shao, Xiao-Wen Wang, Lan-Zhe Guo✉.
Machine Learning, in press, 2022.
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Transfer and Share: Semi-Supervised Learning from Long-Tailed Data.
Tong-Wei, Qian-Yu Liu, Jiang-Xin Shi, Wei-Wei Tu, Lan-Zhe Guo✉.
Machine Learning, in press, 2022.
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Towards Safe Weakly Supervised Learning.
Yu-Feng Li, Lan-Zhe Guo (co-first author), Zhi-Hua Zhou.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(1): 334-346, 2021.
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Interactive Graph Construction for Graph-Based Semi-Supervised Learning.
Chang-Jian Chen, Zhao-Wei Wang, Jing Wu, Xi-Ting Wang, Lan-Zhe Guo, Yu-Feng Li, Shi-Xia Liu.
IEEE Transactions on Visualization and Computer Graphics (TVCG), 27(9): 3701-3716, 2021.
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Learning From Group Supervision: The Impact of Supervision Deficiency on Multi-Label Learning.
Miao Xu, Lan-Zhe Guo✉.
Science China Information Science, 64(3): 1-13, 2021.
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Learning Safe Multi-Label Prediction for Weakly Labeled Data.
Tong Wei, Lan-Zhe Guo, Yu-Feng Li, Wei Gao.
Machine Learning, 107(4): 703-725, 2018.
Thesis
💻 Software
- LAMDA-SSL: A comprehensive and easy-to-use toolkit for semi-supervised learning. LAMDA-SSL contains 30+ semi-supervised learning algorithms, including both statiscal and deep semi-supervised learning. We hope this toolkit can promote the research of semi-supervised learning.
[Tutorial][Github] - LawGPT: A Large Language Model in the Legal Domain.
[Github]
🏆 Awards
- 吴文俊人工智能科学技术奖优秀博士学位论文,人工智能学会,2023
- Excellent Doctoral Dissertation Award, Computer Science and Technology, Nanjing University, 2022
- Baidu Scholarship (10 candidates worldwide each year), Baidu Inc, 2021
- Tecent Scholarship, Nanjing University, 2021
- MSRA Fellowship Nomination Award, 2021
- Outstanding Contribution Award of Huawei-LAMDA Artificial Intelligence Joint Laboratory, 2021
- National Scholarship for Ph.D. Students, Nanjing University, 2020
- GuoRui Scholarship, 2019
- National Scholarship for Master Students, Nanjing University, 2018
- ACM-ICPC Asia Regional Contest, Gold Medal: Shenyang Site, 2015
- ACM-ICPC Asia Regional Contest, Sliver Medal: Mudanjiang Site, 2014
- ACM-ICPC Asia Regional Contest, Sliver Medal: AnShan Site, 2014
- ACM-ICPC Asia Regional Contest, Sliver Medal: BeiJing Site, 2015
- The 2015 China Collegiate Programming Contest, Sliver Medal
- Northeast Collegiate Programming Contest, Second Place, 2015
- CodeCup 2015: Gold Medal
- ACM-ICPC China Provincial Programming Contest, Gold Medal, Jilin, 2014
📖 Educations
- 2019.09 - 2022.06, Ph.D., Computer Science and Technology, Nanjing University, Nanjing.
- 2017.09 - 2019.07, Master, Computer Science and Technology, Nanjing University, Nanjing.
- 2013.09 - 2017.07, Undergraduate, School of Software, Jilin University, Jilin.
📚 Teaching
- 人工智能导论(Introduction to Artificial Intelligence) For undergraduate students, Fall, 2023.
- 高级机器学习(Advanced Machine Learning) For graduate students, Fall, 2023.
🤝 Activities
Conference Committee
- Senior Program Committee Member, IJCAI 2021.
- Senior Program Committee Member, ACML 2021/2022.
- Program Committee Member, NeurIPS 2020/2021/2022/2023.
- Program Committee Member, ICML 2020/2021/2022/2023.
- Program Committee Member, ICLR 2021/2022/2023.
- Program Committee Member, KDD 2020/2021/2022/2023.
- Program Committee Member, IJCAI 2020/2021/2022/2023.
- Program Committee Member, AAAI 2019/2020/2021/2022.
- Program Committee Member, ECML 2023
- Program Committee Member, UAI 2023
Journal Reviewer
- Reviewer for IEEE TKDE, IEEE TSMCS, Artificial Intelligence, Machine Learning, Neural Networks, Pattern Recognition, etc.
Organization Committee
- Organization committee member of MLA 2022.
- Workflow chair of CCML 2021.
- Workflow chair of ACML 2021.
- Assist in the organization of PAKDD Workshop on Weakly Supervised Learning, Apr. 2019, Macau, China.
- Volunteer for MLA 2018.