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 to prospective applicants: All positions for PhDs and masters are filled for class 2021. [Instructions]

Biography


My research spans machine learning theories and algorithms, with special interests in transfer learning, unsupervised learning, and deep learning. My research is persistently dedicated to enabling adaptive machine learning in non-stationary environment, including representation learning, predictive learning, transfer learning, domain adaptation, multitask learning and learning to learn.

I am leading the Machine Learning Group. Our team is interested in developing foundational theories, effective algorithms, and reliable systems of machine learning to enable systematic generalization in data representation, inference, and decision making. We focus on industrial applications of AI and data science, including text, sequence, image, video, and scientific data analysis and understanding.

My Quote

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

Research


Education


Research Experience


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

  2. Conditional Adversarial Domain Adaptation
    Mingsheng Long, Zhangjie Cao, Jianmin Wang, Michael I. Jordan
    Neural Information Processing Systems (NeurIPS), 2018 [PDF] [Poster] [Code]

  3. PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
    Yunbo Wang, Zhifeng Gao, Mingsheng Long*, Jianmin Wang, Philip S. Yu
    International Conference on Machine Learning (ICML), 2018 [PDF] [Code]

  4. 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]

  5. 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]

  6. 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]

  7. 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]

  8. 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]

Invited Talks


Libraries


Awards


Professional Services


PC Chair

Area Chair

Senior PC Member

PC Member | Reviewer

Research Grants


Teaching


Students


PhDs

Masters

Undergraduates