In: Proceedings of the 40th International Conference on Machine Learning ( ICML'23). ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. Zhi-Zhou*, Lan-Zhe Guo, Lin-Han Jia, Ding-Chu Zhang, Yu-Feng Li. Identifying Useful Learnwares for Heterogeneous Label Spaces. Lan-Zhe Guo*, Zhi Zhou, Yu-Feng Li, Zhi-Hua Zhou. How Re-sampling Helps for Long-Tail Learning? In: Advances in Neural Information Processing Systems ( NeurIPS'23), 2023. Jiang-Xin Shi*, Tong Wei, Yuke Xiang, Yu-Feng Li. Chinese Journal of Computers (in chinese with english abstract), 2009, 32(5):946-952. Kwok, and Zhi-Hua Zhou, Combo-Dimensional Kernels for Graph Classification. Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang and Yu-Feng Li. IEEE Transactions on Information Theory ( IEEE TIT). Improved Bounds for the Nystrom Method withĪpplication to Kernel Classification. Rong Jin, Tian-Bao Yang, Mehrdad Mahdavi, Yu-Feng Li and Zhi-Hua Zhou. Yu-Feng Li, Ivor Tsang, James Kwok and Zhi-Hua Zhou. IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI), 37(1):175-188, 2015. Towards Making Unlabeled Data Never Hurt. Classifier Circle Method for Multi-Label Learning. Instance Selection Method for Improving Graph-Based Semi-Supervised Learning. Learning Safe Multi-Label Prediction for Weakly Labeled Data. Tong Wei*, Lan-Zhe Guo, Yu-Feng Li, Wei Gao. Safe Semi-Supervised Learning: A Brief Introduction. IEEE Transactions on Knowledge and Data Engineering ( TKDE). Robust Multi-Label Learning with PRO Loss. IEEE Transactions on Neural Network and Learning Systems ( TNNLS), 31(7): 2315-2324, 2020. Does Tail Label Help for Large-Scale Multi-Label Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence ( TPAMI), 43(1): 334-346, 2021. IEEE Transactions on Knowledge and Data Engineering ( TKDE), 33(5): 2071-2082, 2021. Lightweight Label Propagation for Large-Scale Network Data. IEEE Transactions on Visualization and Computer Graphics ( TVCG), 27(9): 3701-3716, 2021 Interactive Graph Construction for Graph-Based Semi-Supervised Learning. Science CHINA Information Science, 65: 212101, 2022.Ĭhangjian Chen, Zhaowei Wang, Jing Wu, Xiting Wang, Lan-Zhe Guo, Yu-Feng Li, Shixia Liu. Robust Model Selection for PU Learning under Constraint. Tong Wei*, Hai Wang, Wei-Wei Tu, Yu-Feng Li. Science CHINA Information Science (In chinese), In Press. Science CHINA Information Science, In Press. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. Lin-Han Jia*, Lan-Zhe Guo, Zhi Zhou, Yu-Feng Li. Towards Robust Test-Time Adaptation for Open-Set Recognition. Zhi Zhou*, Ding-Chu Zhang, Yu-Feng Li, Min-Ling Zhang. RTS: Learning Robustly from Time Series Data with Noisy Label. Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification. Book Chapter of 'Machine Learning and its Applications' 2015ĪCML 2022 Journal Track Guest Editors: Yu-Feng Li, Prateek Jain, Machine Learning Journal 2023ĪCML 2021 Journal Track Guest Editors: Yu-Feng Li, Mehmet Gonen, Kee-Eung Kim, Machine Learning Journal 2022 Min-Ling Zhang, Qing-Hua Hu and Yu-Feng Li. LaWGPT: We open-sourced a LLM for Chinese Legal domain. LAMDA-SSL contains 30+ semi-supervised learning algorithms, including both statiscal and deep semi-supervised learning. LAMDA-SSL: We provide a comprehensive and easy-to-use toolkit for semi-supervised learning.
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