主要从事机器学习、数据挖掘、人工智能等领域的基础理论和应用研究工作，主要研究方向如下：
[1] 理论层面：非完美数据条件下的机器学习算法，包括多标记学习（Multi-Label Learning）、偏标记学习（Partial-Label Learning）、多视图学习（Multi-View Learning）等。
[2] 应用层面：聚焦于深度学习框架下的多模态数据语义理解及其数据安全研究，包括多标记图像分类（Multi-Label Image Classification）、多模态学习（Multi-Modal Learning）、联邦学习（Federated Learning）等。
个人简介
吕庚育，男，校聘教授，博士生导师。2022年6月毕业于北京交通大学计算机科学与技术专业，获得工学博士学位。同年7月，加入北京工业大学计算机科学与技术系DMS实验室，入选北京工业大学高端人才队伍建设计划。
课题组目前有博士生1名，硕士生3名，本科生2名，氛围和谐融洽、积极向上。课题组提供3090多GPU服务器和4090GPU工作站多台，丰富的计算资源可供学生完成学习和科研任务。欢迎具有【扎实数学基础、良好英文读写水平】以及拥有【强悍编程能力、优秀自我驱动力】的计算机科学与技术、信息与计算科学等相关专业同学报考。
[个人主页][谷歌学术]
教育简历
2018年9月-2022年6月，北京交通大学计算机与信息技术学院，博士
2016年9月-2018年7月，北京交通大学计算机与信息技术学院，硕博连读
2012年9月-2016年7月，北京联合大学信息学院，学士
工作履历
2022年7月至今，银河集团9873.cσm，校聘教授、博导
学术兼职
[1] 学术期刊审稿人：TPAMI、TNNLS、TMM、TCYB、TCSVT、TII、MLJ、INS、软件学报等；
[2] 学术会议程序委员会成员/审稿人：ICML、NeurIPS、ICLR、CVPR、ICCV、ECCV、AAAI、IJCAI、ECML-PKDD等。
科研项目
[1] 中国博士后科学基金项目，2022-2024，主持
[2] 北京工业大学高端人才优秀人才项目，2023-2027，主持
荣誉和获奖
[1] 北京交通大学优秀博士学位论文，2022
[2] 知行奖学金(北京交通大学研究生最高荣誉)，2021
[3] 宝钢优秀学生奖、华为奖学金、博士研究生国家奖学金等，2021
主要论文论著
[1] G. Lyu, S. Feng, T. Wang, C. Lang, Y. Li. GM-PLL: Graph Matching based Partial Label Learning. IEEE Transactions on Knowledge and Data Engineering. 33(2): 521-535. 2021 (CCF A, JCR:Q1, IF:9.235)
[2] G. Lyu, S. Feng, Y. Li. Partial Multi-Label Learning via Probabilistic Graph Matching Mechanism. ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2020: 105-113 (CCF A, Oral)
[3] G. Lyu, X. Deng, Y. Wu, S. Feng. Beyond Shared Subspace: A View-Specific Fusion for Multi-View Multi-Label Learning. AAAI Conference on Artificial Intelligence. 2022 (CCF A)
[4] G. Lyu, Y. Wu, S. Feng. Deep Graph Matching for Partial Label Learning. International Joint Conference on Artificial Intelligence. 2022: 3306-3312 (CCF A)
[5] G. Lyu, S. Feng, T. Wang, C. Lang. A Self-Paced Regularization Framework for Partial Label Learning. IEEE Transactions on Cybernetics. 52(2): 899-911. 2022 (CCF B, JCR:Q1, IF:19.118)
[6] G. Lyu, S. Feng, Y. Jin, T. Wang, C. Lang, Y. Li. Prior Knowledge Regularized Self-Representation Model for Partial Multi-Label Learning. IEEE Transactions on Cybernetics. 2021 (CCF B, JCR:Q1, IF:19.118)
[7] G. Lyu, S. Feng, Y. Li, Y. Jin, C. Lang. HERA: Partial Label Learning by Combining Heterogeneous Loss with Sparse and Low-Rank Regularization. ACM Transactions on Intelligent Systems and Technology. 11(3), 34:1-34, 2020 (JCR:Q1, IF:10.489)
[8] G. Lyu, S. Feng, W. Liu, Y. Li. Redundant Label Learning via Subspace Representation and Global Disambiguation. ACM Transactions on Intelligent Systems and Technology. 2022 (JCR:Q1, IF:10.489)
[9] G. Lyu, S. Feng, S. Wang, Z. Yang. Prior Knowledge Constrained Adaptive Graph Framework for Partial Label Learning. ACM Transactions on Intelligent Systems and Technology. 2022 (JCR:Q1, IF:10.489)
[10] G. Lyu, S. Feng, Y. Li. Partial Label Learning via Self-Paced Curriculum Strategy. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2020: 489-505 (CCF B)
[11] G. Lyu, S. Feng, Y. Li. Noisy Label Tolerance: A New Perspective of Partial Multi-Label Learning. Information Sciences. 543: 541-564, 2021 (CCF B, JCR:Q1, IF:8.233)
[12] Z. Li*, G. Lyu*(equal contribution), S. Feng. Partial Multi-Label Learning via Multi-Subspace Representation. International Joint Conference on Artificial Intelligence. 2020: 2612-2618 (CCF A)
[13] Y. Sun*, G. Lyu*(equal contribution), S. Feng. Partial Label Learning via Subspace Representation and Global Disambiguation. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 2020: 439-454 (CCF B)
[14] Y. Wu, H. Liu, S. Feng, Y. Jin, G. Lyu, Z. Wu. GM-MLIC: Graph Matching based Multi-Label Image Classification. International Joint Conference on Artificial Intelligence. 2021: 1179-1185 (CCF A)
[15] Y. Wu, T. Liang, S. Feng, Y. Jin, G. Lyu, H. Fei, Y. Wang. MetaZSCIL: A Meta-Learning Approach for Generalized Zero-Shot Class Incremental Learning. AAAI Conference on Artificial Intelligence. 2023 (CCF A)
[16] X. Deng, S. Feng, G. Lyu, H. Liu, Y. Jin. Beyond Word Embeddings: Heterogeneous Prior Knowledge Driven Multi-Label Image Classification. IEEE Transactions on Multimedia. 2022 (CCF B, JCR:Q1, IF:8.182)
[17] L. Sun, S. Feng, J. Liu, G. Lyu, C. Lang. Global-Local Label Correlation for Partial Multi-Label Learning. IEEE Transactions on Multimedia. 2020. (CCF B, JCR:Q1, IF:8.182)
[18] X. Lu, S. Feng, G. Lyu, Y. Jin, C. Lang. “Distance-Preserving Embedding Adaptive Bipartite Graph Multi-View Learning with Application to Multi-Label Classification”, ACM Transactions on Knowledge Discovery from Data. 2022. (CCF B, JCR:Q1, IF:4.157)
[19] Z. Gu, S. Feng, R. Hu, G. Lyu. “ONION: Joint Unsupervised Feature Selection and Robust Subspace Extraction for Graph-Based Multi-View Clustering”, ACM Transactions on Knowledge Discovery from Data. 2022. (CCF B, JCR:Q1, IF:4.157)
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