Abstract:

Partial label learning (PLL) is one of the important weakly-supervised learning frameworks. Under the partial label learning framework, each example is associated with multiple candidate labels among which only one is valid. Partial label learning techniques have been widely used in many scenarios including automatic multimedia content annotation, natural language processing, ecoinformatics, etc. In this talk, the state-of-the-art on partial label learning will be introduced from three aspects. Firstly, the problem setting of partial label learning and its relationships to other weakly-supervised learning frameworks are briefly discussed. Secondly, existing works as well as our recent progresses on designing partial label learning algorithms are summarized. Thirdly, related academic resources on partial label learning are given.

Speaker Bio:

Min-Ling Zhang received his PhD degree in computer science from Nanjing University, China, in 2007. Currently, he is a Professor at the School of Computer Science and Engineering, Southeast University, China. Currently, he is a Professor at the School of Computer Science and Engineering, Southeast University, China. His main research interests include machine learning and data mining. In recent years, Dr. Zhang has served as the General Co-Chairs of ACML’18, Program Co-Chairs of PAKDD’19, CCF-ICAI’19, ACML’17, CCFAI’17, PRICAI’16, Senior PC member or Area Chair of AAAI 2017-2020, IJCAI 2017-2022, KDD 2021-2022, ICDM 2015-2022, etc. He is also on the editorial board of IEEE Trans. PAMI, ACM Trans. IST, Machine Intelligence Research, Frontiers of Computer Science, etc. Dr. Zhang is the Steering Committee Member of ACML and PAKDD, Vice Chair of the CAAI Machine Learning Society, standing committee member of the CCF Artificial Intelligence & Pattern Recognition Society.