[W.3]
A Survey on Extreme Multi-label Learning [pdf] [code]
Tong Wei, Zhen Mao, Jiang-Xin Shi, Yu-Feng Li, Min-Ling Zhang
arXiv:2210.03968, 2022.
[W.2]
Robust Long-Tailed Learning under Label Noise [pdf] [code]
Tong Wei*, Jiang-Xin Shi*, Yu-Feng Li
arXiv:2108.11569, 2021.
[W.1]
MixPUL: Consistency-based Augmentation for Positive and Unlabeled Learning [pdf] [code]
Tong Wei, Feng Shi, Wei-Wei Tu, Yu-Feng Li
arXiv:2004.09388, 2020.
[J.5]
Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification [pdf][code]
Jiang-Xin Shi*, Tong Wei*, Yu-Feng Li.
SCIENCE CHINA Information Sciences (SCIS). In Press.
[J.4]
Transfer and Share: Semi-Supervised Learning from Long-Tailed Data [pdf] [code]
Tong Wei*†, Qian-Yu Liu*, Jiang-Xin Shi, Wei-Wei Tu, Lan-Zhe Guo
Machine Learning (MLJ). 2022.
[J.3]
Robust Model Selection for PU Learning under Constraint [pdf]
Tong Wei, Hai Wang, Wei-Wei Tu, Yu-Feng Li
Science CHINA Information Science (SCIS), 65(11): 1-13, 2022.
[J.2]
Does Tail Label Help for Large-Scale Multi-Label Learning [pdf]
Tong Wei, Yu-Feng Li
IEEE Transactions on Neural Network and Learning Systems (TNNLS), 31(7): 2315-2324, 2020.
[J.1]
Learning Safe Multi-Label Prediction for Weakly Labeled Data [pdf] [code]
Tong Wei, Lan-Zhe Guo, Yu-Feng Li, Wei Gao
Machine Learning (MLJ), 107(4): 703-725, 2018.
[C.20]
Efficient and Long-Tailed Generalization for Pre-trained Vision-Language Model [pdf] [code]
Jiang-Xin Shi, Chi Zhang, Tong Wei, Yu-Feng Li.
In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), 2024.
[C.19]
Nearly Optimal Regret for Decentralized Online Convex Optimization [pdf]
Yuanyu Wan, Tong Wei, Mingli Song, Lijun Zhang
In: Proceedings of the 37th Annual Conference on Learning Theory (COLT 2024), 2024.
[C.18]
Erasing the Bias: Fine-Tuning Foundation Models for Semi-Supervised Learning [pdf] [code]
Kai Gan, Tong Wei†
In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 2024.
[C.17]
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition [pdf] [code]
Tong Wei, Zhen Mao, Zi-Hao Zhou, Yuanyu Wan, Min-Ling Zhang
In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 2024.
[C.16]
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts [pdf] [code]
Jiang-Xin Shi*, Tong Wei*, Zhi Zhou, Xin-Yan Han, Jie-Jing Shao, Yu-Feng Li
In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 2024.
[C.15]
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning [pdf] [code]
Bo Ye, Kai Gan, Tong Wei†, Min-Ling Zhang
In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI 2024), Jeju, South Korea, 2024
[C.14]
EAT: Towards Long-Tailed Out-of-Distribution Detection [pdf] [code]
Tong Wei, Bo-Lin Wang, Min-Ling Zhang
In: Proceedings of the 38th AAAI conference on Artificial Intelligence (AAAI 2024), Vancouver, Canada, 2024.
[C.13]
How Re-sampling Helps for Long-Tail Learning? [pdf] [code]
Jiang-Xin Shi*, Tong Wei*, Yuke Xiang, Yu-Feng Li.
In: Advances in Neural Information Processing Systems (NeurIPS 2023), New Orleans, USA. 2023.
[C.12]
Scene-Aware Feature Matching [pdf]
Xiaoyong Lu, Yaping Yan, Tong Wei, Songlin Du
In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2023), Paris, France. 2023, pp.3704-3713.
[C.11]
Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels [pdf] [code]
Hao-Tian Li, Tong Wei†, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, Min-Ling Zhang
In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023), Macau, China. 2023, pp.3902-3910.
[C.10]
Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency Is All You Need [pdf] [code]
Tong Wei, Kai Gan
In: Proceedings of the 34th IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR 2023), Vancouver, Canada, 2023, pp.3469-3478.
[C.9]
Can Label-Specific Feature Help Partial-Label Learning? [pdf] [code]
Ruo-Jing Dong, Jun-Yi Hang, Tong Wei†, Min-Ling Zhang
In: Proceedings of the 37th AAAI conference on Artificial Intelligence (AAAI 2023), Washington, D.C., USA, 2023, pp.7432-7440.
[C.8]
Prototypical Classifier for Robust Class-Imbalanced Learning [pdf] [code]
Tong Wei*, Jiang-Xin Shi*, Yu-Feng Li, Min-Ling Zhang
In: Proceedings of the 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2022, Best Paper Award). Chengdu, China, 2022, 13281:44–57.
[C.7]
NGC: A Unified Framework for Learning with Open-World Noisy Data [pdf] [code]
Zhi-Fan Wu*, Tong Wei*, Jianwen Jiang*, Chaojie Mao, Mingqian Tang, Yu-Feng Li
In: Proceedings of International Conference on Computer Vision (ICCV 2021, Oral), Virtual Event, 2021, pp.62-71.
[C.6]
Probabilistic Label Tree for Streaming Multi-Label Learning [pdf] [code]
Tong Wei*, Jiang-Xin Shi*, Yu-Feng Li
In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), Virtual Event, 2021, pp.1801-1811.
[C.5]
Towards Robust Prediction on Tail Labels [pdf] [code]
Tong Wei, Wei-Wei Tu, Yu-Feng Li, Guo-Ping Yang
In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2021), Virtual Event, 2021, pp.1812-1820.
[C.4]
Learning for Tail Label Data: A Label-Specific Feature Approach [pdf]
Tong Wei, Wei-Wei Tu, Yu-Feng Li
In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), Macau, China. 2019, pp.3842-3848.
[C.3]
Learning Compact Model for Large-Scale Multi-Label Learning [pdf] [code]
Tong Wei, Yu-Feng Li
In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI 2019), Honolulu, HI, 2019, pp.5385-5392.
[C.2]
Towards Automated Semi-Supervised Learning [pdf]
Yu-Feng Li, Hai Wang, Tong Wei, Wei-Wei Tu
In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI 2019), Honolulu, HI, 2019, pp.4237-4244.
[C.1]
Does Tail Label Help for Large-Scale Multi-Label Learning [pdf] [code]
Tong Wei, Yu-Feng Li
In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI 2018), Stockholm, Sweden, 2018, pp.2847-2853.