Call for Papers: NIPS 2009 Workshop on Transfer Learning for Structured
in conjunction with NIPS 2009, Dec 7-12, 2009, Vancouver, B.C., Canada
Description and background
Recently, transfer learning (TL) has gained much popularity as an approach
to reduce the training-data calibration effort as well as to 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
poses 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 to develop methods or systems, and 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.
Topics of Interest
(The topics of interest include but are not limited to the following)
Transfer learning for networked data.
Transfer learning for social networks.
Transfer learning for relational domains.
Transfer learning for non-i.i.d. and/or heterogeneous data.
Transfer learning from multiple structured data sources.
Transfer learning for bioinformatics networks.
Transfer learning for sensor networks.
Theoretical analysis on transfer learning algorithms for structured data.
We encourage authors submit extended abstracts of up
to 4 pages. To encourage that the best work in this field can be presented
TLSD, we also allow authors to submit their published or submitted work of up
to 9 pages. Submissions should be using NIPS style files (available at
http://nips.cc/PaperInformation/StyleFiles), and should include the title,
authors’ names, institutions and email addresses, and a brief abstract.
Accepted papers will be either presented as a talk or poster (with poster
spotlight). Details of submission instructions are available at
Deadline for submissions: October 26, 2009
Notification of acceptance: November 9, 2009
Deadline for camera-ready version: November 26, 2009
Workshop date: December 12, 2009 (Saturday)
Invited Speakers (Confirmed)
Arthur Gretton, Carnegie Mellon University, USA
Shai Ben-David, University of Waterloo, Canada
Sinno Jialin Pan, Hong Kong University of Science and Technology, Hong Kong
Ivor W. Tsang, Nanyang Technological University, Singapore
Le Song, Carnegie Mellon University, USA
Karsten Borgwardt, MPI for Biological Cybernetics, Germany
Qiang Yang, Hong Kong University of Science and Technology, Hong Kong
Andreas Argyriou, Toyota Technological Institute at Chicago, USA
Shai Ben-David, University of Waterloo, Canada
John Blitzer, University of California, USA
Hal Daume III, University of Utah, USA
Jesse Davis, University of Washington, USA
Jing Gao, University of Illinois, Urbana-Champaign, USA
Steven Hoi, Nanyang Technological University, Singapore
Jing Jiang, Singapore Management University, Singapore
Honglak Lee, Stanford University, USA
Lily Mihalkova, University of Maryland, USA
Raymond Mooney, University of Texas at Austin, USA
Massimiliano Pontil, University College London, UK
Masashi Sugiyama, Tokyo Institute of Technology, Japan
Koji Tsuda, AIST Computational Biology Research Center, Japan
Jingdong Wang, Microsoft Research Asia, China
Dong Xu, Nanyang Technological University, Singapore
If you have any questions, please contact us via email@example.com.