[1]. Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated Learning: Transfer Learning across Different Feature Spaces. Advances in Neural Information Processing Systems 21 (NIPS 2008), Vancouver, British Columbia,
Canada, December 8-13, 2008.
[2]. Xiao Ling, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. Spectral Domain-Transfer Learning. In Proceedings of the Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), Pages 488-496,
Las Vegas, Nevada, USA, August 24-27, 2008.
[3]. Wenyuan Dai, Qiang Yang, Gui-Rong Xue and Yong Yu. Self-taught Clustering. In Proceedings of the Twenty-Fifth International Conference on Machine Learning (ICML 2008), pages 200-207, Helsinki, Finland, 5-9 July, 2008.
[4]. Gui-Rong Xue, Wenyuan Dai, Qiang Yang and Yong Yu. Topic-bridged PLSA for Cross-Domain Text Classification. In Proceedings of the Thirty-first International ACM SIGIR Conference on Research and Development on Information Retrieval
(SIGIR2008), pages 627-634, Singapore, July 20-24, 2008.
[5]. Xiao Ling, Gui-Rong Xue, Wenyuan Dai, Yun Jiang, Qiang Yang and Yong Yu. Can Chinese Web Pages be Classified with English Data Source? In Proceedings the Seventeenth International World Wide Web Conference (WWW2008), Pages 969-978,
Beijing, China, April 21-25, 2008.
[6]. Xiao Ling, Wenyuan Dai, Gui-Rong Xue and Yong Yu. Knowledge Transferring via Implicit Link Analysis. In Proceedings of the Thirteenth International Conference on Database Systems for Advanced Applications (DASFAA 2008), Pages 520-528,
New Delhi, India, March 19-22, 2008.
[7]. Wenyuan Dai, Gui-Rong Xue, Qiang Yang and Yong Yu. Co-clustering based Classification for Out-of-domain Documents. In Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007),
Pages 210-219, San Jose, California, USA, Aug 12-15, 2007.
[8]. Wenyuan Dai, Gui-Rong Xue, Qiang Yang and Yong Yu. Transferring Naive Bayes Classifiers for Text Classification. In Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI 2007), Pages 540-545, Vancouver,
British Columbia, Canada, July 22-26, 2007.
[9]. Wenyuan Dai, Qiang Yang, Gui-Rong Xue and Yong Yu. Boosting for Transfer Learning. In Proceedings of the Twenty-Fourth International Conference on Machine Learning (ICML 2007), Pages 193-200, Corvallis, Oregon, USA, June 20-24, 2007.
[10]. Dikan Xing, Wenyuan Dai, Gui-Rong Xue and Yong Yu. Bridged Refinement for Transfer Learning. In Proceedings of the Eleventh European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2007), Pages 324-335,
Warsaw, Poland, September 17-21, 2007. (Best Student Paper Award)
[11]. Xin Zhang, Wenyuan Dai, Gui-Rong Xue and Yong Yu. Adaptive Email Spam Filtering based on Information Theory. In Proceedings of the Eighth International Conference on Web Information Systems Engineering (WISE 2007), Pages 159–170,
Nancy, France, December 3-7, 2007.
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.