张一凡，东南大学计算机科学与工程学院助理研究员。2026年于东南大学获得博士学位，导师为张敏灵教授。主要研究方向为机器学习理论，先后围绕多标记学习、可信机器学习的理论问题开展多项工作。相关工作以第一作者身份在ICML和NeurIPS国际顶级会议上发表CCF-A类论文5篇，其中1篇在机器学习顶会上荣获亮点报告（录用率仅3%）。受邀担任IEEE TPAMI，IEEE TKDE，ICML，NeurIPS等CCF-A类期刊和顶会审稿人。
Y.-F. Zhang, M.-L. Zhang. Tight and fast bounds for multi-label learning. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025, 76909-76938.
Y.-F. Zhang, X. Zhang, M.-L. Zhang. Generalization analysis for controllable learning. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), Vancouver, Canada, 2025, 77017-77043.
Y.-F. Zhang, M.-L. Zhang. Generalization analysis for label-specific representation learning. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, 104904-104933. (spotlight, acceptance rate: 3%)
Y.-F. Zhang, M.-L. Zhang. Generalization analysis for multi-label learning. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, 60220-60243.
Y.-F. Zhang, M.-L. Zhang. Nearly-tight bounds for deep kernel learning. In: Proceedings of the 40th International Conference on Machine Learning (ICML'23), Honolulu, HI, 2023, 41861-41879.
