Connectionists: CFP: NIPS 2005 Workshop: Inductive Transfer (Learning-to-Learn)

[Richard Caruana]

NIPS 2005 Workshop - Inductive Transfer : 10 Years Later 
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Friday Dec 9, Westin Resort & Spa, Whistler, B.C., Canada
 
Overview:
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Inductive transfer refers to the problem of applying the knowledge
learned in one or more tasks to learning for a new task. While all
learning involves generalization across problem instances, transfer
learning emphasizes the transfer of knowledge across domains, tasks,
and distributions that are related, but not the same.  For example,
learning to recognize chairs might help to recognize tables; or
learning to play checkers might improve learning of chess.  While
people are adept at inductive transfer, even across widely disparate
domains, currently we have little learning theory to explain this
phenomena and few systems exhibit knowledge transfer.

At NIPS95 two of the current co-chairs lead a successful two-day
workshop on "Learning to Learn" that focused on lifelong machine
learning methods that retain and reuse learned knowledge. (The
co-organizers of the NIPS95 workshop were Jon Baxter, Rich Caruana,
Tom Mitchell, Lorien Pratt, Danny Silver, and Sebastian Thrun.)  The
fundamental motivation for that meeting was the belief that machine
learning systems would benefit from re-using knowledge learned from
related and/or prior experience and that this would enable them to
move beyond task-specific tabula rasa systems.  The workshop resulted
in a series of articles published in a special issue of Connection
Science [CS 1996], Machine Learning [vol. 28, 1997] and a book
entitled "Learning to Learn" [Pratt and Thrun 1998].

Research in inductive transfer has continued since 1995 under a
variety of names: learning to learn, life-long learning, knowledge
transfer, transfer learning, multitask learning, knowledge
consolidation, context-sensitive learning, knowledge-based inductive
bias, meta-learning, and incremental/cumulative learning.  The recent
burst of activity in this area is illustrated by the research in
multi-task learning within the kernel and Bayesian contexts that has
established new frameworks for capturing task relatedness to improve
learning [Ando and Zhang 04, Bakker and Heskes 03, Jebara 04,
Evgeniou, and Pontil 04, Evgeniou, Micchelli and Pontil 05, Chapelle
and Harchaoui 05].  This NIPS 2005 workshop will examine the progress
that has been made in ten years, the questions and challenges that
remain, and the opportunities for new applications of inductive
transfer systems.

In particular, the workshop organizers have identified three major
goals:

(1) To summarize the work thus far in inductive transfer to develop a
taxonomy of research and highlight open questions,

(2) To share new theories, approaches, and algorithms regarding the
accumulation and re-use of learned knowledge to make learning more
effective and more efficient,

(3) To discuss the formation of an inductive transfer special interest
group that might offer a website, benchmarking data, shared software,
and links to various research programs and related web resources.

Call for Papers:
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We invite submission of workshop papers that discuss ongoing or
completed work dealing with Inductive Transfer (see below for a list
of appropriate topics).  Papers should be no more than four pages in
the standard NIPS format.  Authorship should not be blind. Please
submit a paper by emailing it in Postscript or PDF format to
danny.silver at acadiau.ca with the subject line "ITWS Submission". We
anticipate accepting as many as 8 papers for 15 minute presentation
slots and up to 20 poster papers.  Please only submit an article if at
least one of the authors will attend the workshop to present the work.

The successful papers will be made available on the Web.  A special
journal issue or an edited book of selected papers also is being
planned. 

The 1995 workshop identified the most important areas for future
research to be:

* The relationship between computational learning theory and selective
inductive bias;

* The tradeoffs between storing or transferring knowledge in
representational and functional form;

* Methods of turning concurrent parallel learning into sequential 
lifelong learning methods;

* Measuring relatedness between learning tasks for the purpose of 
knowledge transfer;

* Long-term memory methods and cumulative learning; and

* The practical applications of inductive transfer and lifelong 
learning systems. 

The workshop is interested in the progress that has been made in these
areas over the last ten years.  These remain key topics for discussion
at the proposed workshop.

More forward looking and important questions include:

* Under what conditions is inductive transfer difficult? When is it 
easy?

* What are the fundamental requirements for continual learning and 
transfer?

* What new mathematical models/frameworks capture/demonstrate transfer
learning? 

* What are some of latest and most advanced demonstrations of transfer
learning in machines 

* What can be learned from transfer learning in humans and animals? 

* What are the latest psychological/neurological/computational theories of
knowledge transfer in learning?

Important Dates:
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19 Sep 05 - Call for participation
28 Oct 05 - Paper submission deadline
11 Nov 05 - Notification of paper acceptance
09 Dec 05 - Workshop in Whistler

Organizers:
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Goekhan Bakir,       Max Planck Institute for Biological Cybernetics, Germany 
Kristin Bennett,     Department of Mathematical Sciences, Rensselaer Polytechnic Institute, USA 
Rich Caruana,        Department of Computer Science, Cornell University, USA 
Massimiliano Pontil, Dept. of Computer Science, University College London, UK
Stuart Russell,      Computer Science Division, University of California, Berkeley, USA 
Danny Silver,        Jodrey School of Computer Science, Acadia University, Canada 
Prasad Tadepalli,    School of Electrical Eng. and Computer Science, Oregon State University, USA 

For further Information:
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See the workshop webpage at http://iitrl.acadiau.ca/itws05/ 
or email danny.silver at acadiau.ca