Leveraging Unlabeled Data: From ‘pure learning’ to learning + reasoning
Talk outline: It is generally expensive or even infeasible to collect a huge amount of labeled training data in many practical applications, and therefore, leveraging unlabeled data is attracting more and more attention. In this talk, we will briefly introduce the efforts of leveraging unlabeled data, from “pure learning” solutions that exploit unlabeled data by using machine learning only, to a recent “learning + reasoning” solution that exploits machine learning and logical reasoning in a balanced and mutually beneficial way, where the utilization of logical reasoning offers the possibility of exploiting domain knowledge, and even possibility of knowledge discovery or refinement based on observed data.
Bio: Zhi-Hua Zhou is Professor of Computer Science and Artificial Intelligence at Nanjing University. His research interests are mainly in machine learning and data mining, with significant contributions to ensemble methods, weakly supervised and multi-label learning. He has authored the books ‘Ensemble Methods: Foundations and Algorithms’, ’Machine Learning (in Chinese)‘, etc., and published more than 200 papers in top-tier journals or conferences. According to Google Scholar, his publications received 60,000+ citations, with H-index 105. Many of his inventions have been successfully applied in industry. He founded ACML (Asian Conference on Machine Learning), served as Program Chair for AAAI-19, IJCAI-21, etc., General Chair for ICDM’16, PAKDD’19, etc., and Senior Area Chair for NeurIPS and ICML. He is on the advisory board of AI Magazine, and associate editor of AIJ, MLJ, IEEE TPAMI, ACM TKDD, etc. He is a Fellow of the ACM, AAAI, AAAS, IEEE, and recipient of the National Natural Science Award of China, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the CCF-ACM Artificial Intelligence Award, etc.