Mingsheng Long


Mingsheng Long

Associate Professor, PhD Supervisor

Machine Learning Group
School of Software
Tsinghua University

longmingsheng@gmail.com
mingsheng@tsinghua.edu.cn
Room 11-413, East Main Building, Tsinghua University, Beijing, China

Reminder: There are no opening positions for PhD or Master students in Class 2022. [Instructions]

Biography


My research spans theories, algorithms, and applications of machine learning, with persistent dedication to enable adaptive machine learning in the open, dynamic and non-stationary world. My current research is to make advances in transfer and multi-task learning, sample-efficient learning, spacetime deep learning, and learning with scientific knowledge.

Our Machine Learning Group is interested in developing machine learning technology for systematic generalization in representation, inference, and decision making with good trade-off between accuracy, efficiency, and stability. We focus on industrial applications of AI and data science, including analysis and understanding of sequence, spacetime, and scientific data.

My Quote

Everything should be made as simple as possible, but no simpler.” --Albert Einstein

Research Interests


Education


Research Experience


Recent News


Selected Publications [Full List] [DBLP] [Google Scholar]

(* Corresponding Author)


Thesis

  1. Mingsheng Long. Transfer Learning: Problems and Methods. 1-127, 2014 [PDF] (In Chinese)

Journal Articles

  1. Transferable Representation Learning with Deep Adaptation Networks
    Mingsheng Long, Yue Cao, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(12):3071-3085, 2019 [Link]

  2. Adaptation Regularization: A General Framework for Transfer Learning
    Mingsheng Long, Jianmin Wang, Guiguang Ding, Sinno Jialin Pan, Philip S. Yu
    IEEE Transactions on Knowledge and Data Engineering (TKDE), 26(5):1076-1089, 2014 [Link] [Code]

Conference Proceedings

  1. LogME: Practical Assessment of Pre-trained Models for Transfer Learning
    Kaichao You, Yong Liu, Jianmin Wang, Mingsheng Long*
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix]

  2. Representation Subspace Distance for Domain Adaptation Regression
    Xinyang Chen, Sinan Wang, Jianmin Wang, Mingsheng Long*
    International Conference on Machine Learning (ICML), 2021 [PDF] [Appendix]

  3. Bridging Theory and Algorithm for Domain Adaptation
    Yuchen Zhang, Tianle Liu, Mingsheng Long*, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2019 [PDF] [Appendix] [Code]

  4. Conditional Adversarial Domain Adaptation
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code] (500+ Citations)
    PaperDigest Most Influential Papers

  5. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
    Yunbo Wang, Mingsheng Long*, Jianmin Wang, Zhifeng Gao, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF]

  6. Learning Multiple Tasks with Multilinear Relationship Networks
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Philip S. Yu
    Neural Information Processing Systems (NeurIPS), 2017 [PDF] [Poster] [Code]

  7. Deep Transfer Learning with Joint Adaptation Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2017 [PDF] [Slides] [Poster] [Code] (1000+ Citations)
    PaperDigest Most Influential Papers

  8. Unsupervised Domain Adaptation with Residual Transfer Networks
    Mingsheng Long, Han Zhu, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2016 [PDF] [Poster] [Code] (800+ Citations)

  9. Learning Transferable Features with Deep Adaptation Networks
    Mingsheng Long, Yue Cao, Jianmin Wang, Michael I. Jordan
    International Conference on Machine Learning (ICML), 2015 [PDF] [Slides] [Poster] [Code] (2000+ Citations)
    PaperDigest Most Influential Papers

Invited Talks


Software


Awards


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PC Chair

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Guest Editor

Senior PC Member

PC Member | Reviewer

Research Grants


Teaching


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Undergraduates