🤵🏻 About Me

I am a researcher with research interests in artificial intelligence and machine learning. 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).

The long-term research goal of our team is to make AI models more general (i.e., able to solve more tasks better). Most recently, the research directions mainly include:

• How to make AI models applicable to more tasks?
• How to adapt pre-trained models to downstream tasks with limited data and labels?
• How to exploit general models in conjunction with task-specific models to solve more tasks?
• How to make AI models adapt to more open environments?
• How to make AI models adapt to the continuous changes in data distributions/classes/features in open environments?

📢: 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，请附上带普通生活照的个人简历、成绩单以及一页纸的“研究动机说明”。

🎉 News

• 2024.01: One Paper is accepted by ICLR 2024!
• 2023.12: CFP: We are organizing a "Robust Machine Learning in Open Environments" workshop in PAKDD 2024, Taipei, May 7, 2024
• 2023.12: One Paper is accepted by IEEE TVCG!
• 2023.11: One paper is accepted by Science China Information Science!
• 2023.08: One paper is accepted by 《中国科学：信息科学》!
• 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!
• 2022.07: We open-sourced the semi-supervised learning toolkit LAMDA-SSL.⛏️ 🛠️

📝 Publications

Conference Papers

• Realistic Evaluation of Semi-Supervised Learning Algorithms in Open Environments..
  Lin-Han Jia, Lan-Zhe Guo^✉, Yu-Feng Li^✉.
  In: Proceedings of the 12th International Conference on Learning Representations (ICLR 2024), Vienna, Austria, 2024.
  [Project Page]
• 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.
  
• 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.
  
• 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.
  
• DualMatch: Robust Semi-Supervised Learning with Dual-Level Interaction.
  Cong Wang, Xiao-Feng Cao, Lan-Zhe Guo, Zeng-Lin Shi.
  In: ECML/PKDD 2023, Turin, Italy, 2023. Page: 102--119.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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

• Interactive Reweighting for Mitigating Label Quality Issues.
  Wei-Kai Yang, Yu-Kai Guo, Jing Wu, Zheng Wang, Lan-Zhe Guo, Yu-Feng Li, Shixia Liu.
  IEEE Transactions on Visualization and Computer Graphics (TVCG), in press, 2024.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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.
  
• 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

• Semi-Supervised Learning for Open Environment.

💻 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.

🎈 Students

Graduate Students

• Hao-Zhe Tan, 2023--
• Zi-Kang Wang, 2023--
• Song-Lin Lv, 2024--
• Tian-Chi Ma, 2024--
• Zi-Jian Cheng, 2024--

🤝 Activities

Conference Committee

• Senior Program Committee Member, IJCAI 2021.
• Senior Program Committee Member, ACML 2021/2022.
• Program Committee Member, NeurIPS 2020--
• Program Committee Member, ICML 2020--
• Program Committee Member, ICLR 2021--
• Program Committee Member, KDD 2020--
• Program Committee Member, IJCAI 2020--
• Program Committee Member, AAAI 2019--
• Program Committee Member, ECML 2023--
• Program Committee Member, UAI 2023--
• Program Committee Member, ECAI 2024--

Journal Reviewer

• Reviewer for IEEE TKDE, IEEE TSMCS, Artificial Intelligence, Machine Learning, Neural Networks, Pattern Recognition,etc.

Organization Committee

• Organizer of PAKDD Workshop on Robust Machine in Open Environments, May, 2024, Taipei, China.
• Organization committee member of MLA 2022.
• Workflow chair of CCML 2021.
• Workflow chair of ACML 2021/2022.
• Assist in the organization of PAKDD Workshop on Weakly Supervised Learning, Apr. 2019, Macau, China.