I am currently a senior researcher at Tencent, focusing on dataset shift in machine learning, especially transfer learning, domain adaptation, cross-domain recommendation, multi-domain learning and multi-task learning. Prior to that, I received my Ph.D. degree from School of Software, Tsinghua University, advised by Prof. Jianmin Wang and Prof. Mingsheng Long. During my Ph.D. study, I was a research intern in Tencent for a year, mentored by Junwei Pan. I received my B.S. degree from Department of Automation, Tsinghua University.
messixmwang [AT] tencent.com, wxm17 [AT] tsinghua.org.cn, wangximei06 [AT] 163.com
[Google Scholar]
[Semantic Scholar]
[ResearchGate]
Binhai Building, Tencent, Shenzhen, China
[2024/05/17] Two papers on recommendation models are accepted by KDD 2024, congrats!
[2024/05/02] Our study on large recommendation models is accepted by ICML 2024, congrats!
[2024/04/07] Our study on domain adaptation is accepted by TPAMI 2024, congrats!
[2024/02/28] Two pre-prints on recommendation models are publicly available!
[2023/12/10] Two papers on multi-domain/task learning are accepted by AAAI 2024, congrats!
[2023/09/22] Our study on auxiliary-task learning is accepted by NeurIPS 2023, congrats! [pdf]
[2023/06/06] Our study on transfer learning is accepted by ECML 2023, congrats! [pdf]
[2023/04/25] Our study on out-of-distributions generalization is accepted by ICML 2023, congrats! [pdf]
[2023/01/11] Our study on multi-task learning is accepted by AAAI 2023 as Oral, congrats! [pdf]
[2022/10/25] Our study on semi-supervised learning is accepted by NeurIPS 2022 as Oral, congrats! [pdf]
[2022/10/08] We are hiring!! If you are interested in the research internship at our group, please drop me an email.
[2022/06/28] I graduated from Tsinghua University and joined Tencent. Thank my advisors!!
Ph.D. in Software Engineering, 2017-2022
School of Software, Tsinghua University, Beijing, China
Bachelor in Automation, 2013-2017
Department of Automation, Tsinghua University, Beijing, China
Long-Sequence Recommendation Models Need Decoupled Embeddings
Ningya Feng, Junwei Pan, Jialong Wu, Baixu Chen, Ximei Wang, Qian Li, Xian Hu, Jie Jiang, Mingsheng Long
Preprint: [arXiv]
Disentangled Representation with Cross Experts Covariance Loss for Multi-Domain Recommendation
Zhutian Lin, Junwei Pan, Haibin Yu, Xi Xiao, Ximei Wang, Zhixiang Feng, Shifeng Wen, Shudong Huang, Lei Xiao, Jie Jiang
Preprint: [arXiv]
Ad Recommendation in a Collapsed and Entangled World
Junwei Pan, Wei Xue, Ximei Wang, Haibin Yu, Xun Liu, Shijie Quan, Xueming Qiu, Dapeng Liu, Lei Xiao, Jie Jiang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024 (Accepted) [arXiv]
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024 (Accepted) [arXiv]
On the Embedding Collapse when Scaling up Recommendation Models
Xingzhuo Guo, Junwei Pan, Ximei Wang, Baixu Chen, Jie Jiang, Mingsheng Long
International Conference on Machine Learning (ICML), 2024 (Accepted) [arXiv]
One Fits Many: Class Confusion Loss for Versatile Domain Adaptation
Ying Jin, Zhangjie Cao, Ximei Wang, Jianmin Wang, and Mingsheng Long
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024 (Accepted)
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Ximei Wang, Junwei Pan, Xingzhuo Guo, Dapeng Liu, Jie Jiang
AAAI Conference on Artificial Intelligence (AAAI), 2024 [arXiv]
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
AAAI Conference on Artificial Intelligence (AAAI), 2024 [arXiv]
CLIPood: Generalizing CLIP to Out-of-Distributions
Yang Shu, Xingzhuo Guo, Jialong Wu, Ximei Wang, Jianmin Wang, Mingsheng Long
International Conference on Machine Learning (ICML), 2023 [PDF] [arXiv] [Code]
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Junguang Jiang, Baixu Chen, Junwei Pan, Ximei Wang, Liu Dapeng, Jie Jiang, Mingsheng Long
Neural Information Processing Systems (NeurIPS), 2023 [arXiv]
Bi-Tuning: Efficient Transfer from Pre-Trained Models
Jincheng Zhong, Haoyu Ma, Ximei Wang, Zhi Kou, Mingsheng Long
European Conference on Machine Learning (ECML), 2023 [PDF] [arXiv] [Code]
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
Enneng Yang, Junwei Pan, Ximei Wang, Haibin Yu, Li Shen, Xihua Chen, Lei Xiao, Jie Jiang, Guibing Guo
AAAI Conference on Artificial Intelligence (AAAI), 2023 [pdf] (Oral)
Debiased Self-Training for Semi-Supervised Learning
Baixu Chen, Junguang Jiang, Ximei Wang, Pengfei Wan, Jianmin Wang, Mingsheng Long
Neural Information Processing Systems (NeurIPS), 2022 [PDF] [arXiv] [PDF] [Code] (Oral)
X-model: Improving Data Efficiency in Deep Learning with A Minimax Model
Ximei Wang, Xinyang Chen, Jianmin Wang, Mingsheng Long
International Conference on Learning Representations (ICLR), 2022 [OpenReview]
Self-Tuning for Data-Efficient Deep Learning
Ximei Wang*, Jinghan Gao*, Jianmin Wang, Mingsheng Long#
International Conference on Machine Learning (ICML), 2021 [PDF] [Code] [Slide] [Video] [Poster] [Blog] [Zhihu] [SlidesLive]
Regressive Domain Adaptation for Unsupervised Keypoint Detection
Junguang Jiang, Yifei Ji, Ximei Wang, Yufeng Liu, Jianmin Wang, Mingsheng Long
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 [PDF] [Code]
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
Ximei Wang, Mingsheng Long#, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2020 [PDF] [Appendix] [Code] [Poster] [Slide] [Video]
Resource Efficient Domain Adaptation
Junguang Jiang, Ximei Wang, Mingsheng Long#, Jianmin Wang
ACM International Conference on Multimedia (ACMMM), 2020 [PDF] [Code]
Minimum Class Confusion for Versatile Domain Adaptation
Ying Jin, Ximei Wang, Mingsheng Long#, Jianmin Wang
European Conference on Computer Vision (ECCV), 2020 [PDF] [Code]
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Ximei Wang, Ying Jin, Mingsheng Long#, Jianmin Wang, Michael I. Jordan
Neural Information Processing Systems (NeurIPS), 2019 [PDF] [Code] [Poster] [Slide]
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
Kaichao You, Ximei Wang, Mingsheng Long#, Michael I. Jordan
International Conference on Machine Learning (ICML), 2019 [PDF] [Code]
Transferable Attention for Domain Adaptation
Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long#, Jianmin Wang
AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF] (Oral)