[UAI] CFP: Transfer Learning workshop at ICML-06 (apologies for multiple postings)

 			   CALL FOR PAPERS

 	  Structural Knowledge Transfer for Machine Learning


   Workshop at the 23rd International Conference on Machine Learning
 			     (ICML 2006)

 	     http://www.cs.utexas.edu/~banerjee/icmlws06/

                 **Submission Deadline: May 02, 2006 **

 		    Carnegie Mellon University, PA
                              June 29, 2006

Transfer learning concerns applying knowledge learned in one (or a set of)
task(s) or domain(s), the "source", to improve learning on another (or a set 
of) related task(s) or domain(s), the "target". Human learning greatly benefits 
from transfer, but its application in machine learning has received significant 
attention only recently. Although the philosophy of transfer has underlined 
much of AI and ML research, there has been recent interest in the machine 
learning community to overtly exploit knowledge transfer for learning faster 
and possibly better solutions. Transfer can be especially effective when the 
learned knowledge is suitably structured, for example, in a relational or 
hierarchical fashion. This workshop is devoted to transfer learning in all 
subareas of machine learning, including but not limited to, concept learning, 
clustering, reinforcement learning, analogy, and to applications and evaluation 
methodologies, with an emphasis on how the learned knowledge is structured and 
exploited.


The workshop will host presentation of short papers (2-4 pages),
invited talks, and panel discussions that lead to new methodologies
and applications for transfer learning. We welcome submissions from
experimentalists, theorists, and psychological modelers with
contributions that deal with transfer in classification, inference,
reactive control, and planning tasks. We also welcome work cast in
logical, probabilistic, and other theoretical frameworks. We
particularly seek contributions from logic and KR communities to
better understand the conditions under which successful and useful
transfer can be expected.  While humans routinely transfer knowledge
and expertise, the underlying mechanisms are not well understood. As
such, another important component of the program will be comparing
proposed methods for transfer against human transfer, and attempting a
deeper understanding of human transfer. We invite computational
psychologists to share their experience in this regard and promote
dialog with machine learning researchers.

SUBMISSION

Submitted papers should be no longer than 4 pages, and should be
formatted according to the ICML guidelines. The complete guidelines
can be found in
http://www.icml2006.org/icml2006/templates.html

Papers should be submitted by May-02-06 in PDF format by email to one of the 
co-chairs. Please see the contact information at 
http://www.cs.utexas.edu/~banerjee/icmlws06/contact.htm for the email 
addresses. Selected papers will be allotted time for presentation and 
discussion at the program and will also be published in an online proceedings 
to be made available at the workshop website before the event.

ORGANIZATION

The Organizing Committee includes:

Tom Fawcett (ISLE)
Lawrence Holder (The University of Texas at Arlington)
Pat Langley (ISLE, Stanford University)
Michael Littman (Rutgers University)
Jude Shavlik (University of Wisconsin-Madison)
Peter Stone (The University of Texas at Austin)
Kurt VanLehn (University of Pittsburgh)
Michael Witbrock (Cycorp, Inc)

The co-chairs are:

Bikramjit Banerjee (The University of Texas at Austin)
Yaxin Liu (The University of Texas at Austin)
G. Michael Youngblood (The University of Texas at Arlington)

For contact information and other details please see the workshop
webpage at

http://www.cs.utexas.edu/~banerjee/icmlws06/


