Anke Tang

@article{tang2025zero,
  title = {Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Xie, Shuai and Hu, Han and Zhang, Lefei and Du, Bo and Tao, Dacheng},
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year = {2025},
  publisher = {IEEE},
}

@article{tang2025data,
  title = {Data-adaptive weight-ensembling for multi-task model fusion},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Liu, Shiwei and Hu, Han and Du, Bo and Tao, Dacheng},
  journal = {International Journal of Computer Vision},
  pages = {1--17},
  year = {2025},
  publisher = {Springer US},
}

@article{tang2025merging,
  title = {Merging models on the fly without retraining: A sequential approach to scalable continual model merging},
  author = {Tang, Anke and Yang, Enneng and Shen, Li and Luo, Yong and Hu, Han and Du, Bo and Tao, Dacheng},
  journal = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems},
  year = {2025},
}

@article{yang2025continual,
  title = {Continual Model Merging without Data: Dual Projections for Balancing Stability and Plasticity},
  author = {Yang, Enneng and Tang, Anke and Shen, Li and Guo, Guibing and Wang, Xingwei and Cao, Xiaochun and Jie, Zhang},
  journal = {The Thirty-Ninth Annual Conference on Neural Information Processing Systems},
  year = {2025},
}

@inproceedings{shentargeted,
  title = {Targeted Low-rank Refinement: Enhancing Sparse Language Models with Precision},
  author = {Shen, Li and Tang, Anke and Luo, Yong and Sun, Tao and Hu, Han and Cao, Xiaochun},
  booktitle = {Forty-second International Conference on Machine Learning},
  year = {2025},
}

@inproceedings{weimodeling,
  title = {Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent},
  author = {Wei, Yongxian and Tang, Anke and Shen, Li and Hu, Zixuan and Yuan, Chun and Cao, Xiaochun},
  booktitle = {Forty-second International Conference on Machine Learning},
  year = {2025},
}

@inproceedings{yang2025mitigating,
  title = {Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace},
  author = {Yang, Jinluan and Tang, Anke and Zhu, Didi and Chen, Zhengyu and Shen, Li and Wu, Fei},
  booktitle = {The 13th International Conference on Learning Representations (ICLR)},
  year = {2025},
}

@article{zheng2025learning,
  title = {Learning from models beyond fine-tuning},
  author = {Zheng, Hongling and Shen, Li and Tang, Anke and Luo, Yong and Hu, Han and Du, Bo and Wen, Yonggang and Tao, Dacheng},
  journal = {Nature Machine Intelligence},
  volume = {7},
  number = {1},
  pages = {6--17},
  year = {2025},
  publisher = {Nature Publishing Group UK London},
}

@inproceedings{tang2024merging,
  title = {Merging Multi-Task Models via Weight-Ensembling Mixture of Experts},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Yin, Nan and Zhang, Lefei and Tao, Dacheng},
  booktitle = {The 41th International Conference on Machine Learning (ICML)},
  year = {2024},
}

@inproceedings{tang2024parameter,
  title = {Parameter efficient multi-task model fusion with partial linearization},
  author = {Tang, Anke and Shen, Li and Luo, Yong and Zhan, Yibing and Hu, Han and Du, Bo and Chen, Yixin and Tao, Dacheng},
  booktitle = {the 12th International Conference on Learning Representations},
  year = {2024},
}

@inproceedings{tang2023improving,
  title = {Improving Heterogeneous Model Reuse by Density Estimation},
  author = {Tang, Anke and Luo, Yong and Hu, Han and He, Fengxiang and Su, Kehua and Du, Bo and Chen, Yixin and Tao, Dacheng},
  booktitle = {Thirty-Second International Joint Conference on Artificial Intelligence},
  year = {2023},
}