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Seminar abstract

Transfer Learning and Applications

Qiang Yang
Huawei Noah’s Ark Lab
Department of Computer Science and Engineering, Hong Kong University of Science and Technology

Abstract: In machine learning and data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains. Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer learning for challenging data mining problems. I will present some theoretical challenges to transfer learning, survey the solutions to them, and discuss several innovative applications of transfer learning, including learning in heterogeneous cross-media domains and in online recommendation, social media and social network mining.

Bio: Prof. Qiang Yang is the head of Huawei Noah’s Ark Lab in Hong Kong. He has been a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology (HKUST) since 2007. Prior to joining HKUST, he had been a faculty member at the University of Waterloo and Simon Fraser University in Canada. He is an IEEE Fellow, IAPR Fellow and ACM Distinguished Scientist. His research interests are data mining and artificial intelligence. Qiang received his PhD from the University of Maryland, College Park in 1989. His research teams won the 2004 and 2005 ACM KDDCUP competitions on data mining. He is the vice chair of ACM SIGART, the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST), and organizer for many international conferences and workshops, including the PC Co-chair for ACM KDD 2010, the General Chair for ACM KDD 2012 in Beijing and PC Chair for IJCAI 2015 Conference in Argentina.
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