Ximei Wang (王希梅)

Ximei Wang

I am currently a quantitative researcher at NewDAQ Investment, focusing on analyzing financial data and deriving insights using mathematical, statistical, and computational methods. Prior to that, I worked at Tencent for nearly three years, focusing on developing ranking algorithms for advertisements. I received my Ph.D. degree from School of Software, Tsinghua University, advised by Prof. Jianmin Wang and Prof. Mingsheng Long, where I focused on transfer learning, semi-supervised learning, and domain adaptation. Before that, I received my B.S. degree from Department of Automation, Tsinghua University.

wangxm [AT] newdaq.com, wxm17 [AT] tsinghua.org.cn, wangximei06 [AT] 163.com
[Google Scholar] [Semantic Scholar] [ResearchGate]

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Publications

(* Equal Contribution, # Corresponding Author)

  1. Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation
    Zhutian Lin, Junwei Pan, Haibin Yu, Xi Xiao, Ximei Wang, Zhixiang Feng, Shifeng Wen, Shudong Huang, Lei Xiao, Jie Jiang
    The Conference on Information and Knowledge Management (CIKM), 2024 (Accepted) [arXiv]

  2. 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
    International Conference on Learning Representations ( ICLR ) , 2025 [OpenReview]

  3. 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]

  4. 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]

  5. 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]

  6. 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)

  7. 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]

  8. 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]

  9. 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]

  10. 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]

  11. 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]

  12. 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)

  13. 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)

  14. 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]

  15. 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]

  16. 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]

  17. 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]

  18. Resource Efficient Domain Adaptation
    Junguang Jiang, Ximei Wang, Mingsheng Long#, Jianmin Wang
    ACM International Conference on Multimedia (ACMMM), 2020 [PDF] [Code]

  19. Minimum Class Confusion for Versatile Domain Adaptation
    Ying Jin, Ximei Wang, Mingsheng Long#, Jianmin Wang
    European Conference on Computer Vision (ECCV), 2020 [PDF] [Code]

  20. 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]

  21. 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]

  22. Transferable Attention for Domain Adaptation
    Ximei Wang, Liang Li, Weirui Ye, Mingsheng Long#, Jianmin Wang
    AAAI Conference on Artificial Intelligence (AAAI), 2019 [PDF] (Oral)

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