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Department of Computer Science Seminar
2012 Series

Multi-Task Learning and Its Applications

Dr. Zhang Yu
Department of Computer Science and Engineering
The Hong Kong University of Science and Technology

Date: February 14, 2012 (Tuesday)
Time: 11:30 am - 1:00 pm
Venue: RRS905, Sir Run Run Shaw Building, Ho Sin Hang Campus

For many real-world machine learning applications, labeled data are costly because the data labeling process is laborious and time consuming. As a consequence, only limited labeled data are available for model training. In the machine learning research community, several directions have been pursued to address this problem and among these efforts, a promising direction is multi-task learning which is a learning paradigm that seeks to boost the generalization performance of a model on a learning task with the help of some other related tasks. This learning paradigm has been inspired by human learning activities in that people often apply the knowledge gained from previous learning tasks to help learn a new task more efficiently and effectively. Of the several approaches proposed in previous research for multi-task learning, a relatively less studied yet very promising approach is based on automatically learning the relationships among tasks from data. In this talk, a probabilistic approach is introduced to learn the task relationships. Besides these, some applications of multi-task learning, i.e., in computer vision, bioinformatics, collaborative filtering, sentiment classification and robot dynamics, will be present.

Dr. Zhang is a Postdoctoral Research Associate at the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology. He received his PhD degree from the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology in 2011, and the BSc and MEng degrees from the Department of Computer Science and Technology, Nanjing University in 2004 and 2007 respectively. His research interests mainly include machine learning and data mining, especially in multi-task learning, transfer learning, dimensionality reduction, metric learning and semi-supervised learning. He published about 20 papers on related topics and was the winner of the best paper award in the 26th Conference on Uncertainty in Artificial Intelligence (UAI) 2010.

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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University