Hong Kong University of Science and Technology; Nanyang Technological University; Carnegie Mellon University; MPIs Tuebingen; Hong Kong University of Science and Technology
Transfer Learning for Structured Data
7:30am - 6:30pm Saturday, December 12, 2009
Recently, transfer learning (TL) has gained much popularity as an approach to reduce the training-data calibration effort as well as improve generalization performance of learning tasks. Unlike traditional learning, transfer learning methods make the best use of data from one or more source tasks in order to learn a target task. Many previous works on transfer learning have focused on transferring the knowledge across domains where the data are assumed to be i.i.d. In many real-world applications, such as identifying entities in social networks or classifying Web pages, data are often intrinsically non i.i.d., which present a major challenge to transfer learning. In this workshop, we call for papers on the topic of transfer learning for structured data. Structured data are those that have certain intrinsic structures such as network topology, and present several challenges to knowledge transfer. A first challenge is how to judge the relatedness between tasks and avoid negative transfer. Since data are non i.i.d., standard methods for measuring the distance between data distributions, such as KL divergence, Maximum Mean Discrepancy (MMD) and A-distance, may not be applicable. A second challenge is that the target and source data may be heterogeneous. For example, a source domain is a bioinformatics network, while a target domain may be a network of webpage. In this case, deep transfer or heterogeneous transfer approaches are required. Heterogeneous transfer learning for structured data is a new area of research, which concerns transferring knowledge between different tasks where the data are non-i.i.d. and may be even heterogeneous. This area has emerged as one of the most promising areas in machine learning. In this workshop, we wish to boost the research activities of knowledge transfer across structured data in the machine learning community. We welcome theoretical and applied disseminations that make efforts (1) to expose novel knowledge transfer methodology and frameworks for transfer mining across structured data. (2) to investigate effective (automated, human-machined-cooperated) principles and techniques for acquiring, representing, modeling and engaging transfer learning on structured data in real-world applications. This workshop on Transfer learning for structured data will bring active researchers in artificial intelligence, machine learning and data mining together toward developing methods or systems together, to explore methods for solving real-world problems associated with learning on structured data. The workshop invites researchers interested in transfer learning, statistical relational learning and structured data mining to contribute their recent works on the topic of interest.