NIPS*95 Workshop: Call for Participation

[Rich Caruana]

                *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
                *-*  POST-NIPS*95 WORKSHOP  *-*
                *-*   December 1-2, 1995    *-*
                *-*     Vail, Colorado      *-*
                *-*-*-*-*-*-*-*-*-*-*-*-*-*-*-*
                *-*  CALL FOR PARTICIPATION *-*
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TITLE:   "Learning to Learn: Knowledge Consolidation 
             and Transfer in Inductive Systems"

ORGANIZERS:   Jon Baxter, Rich Caruana, Tom Mitchell, 
              Lori Pratt, Danny Silver, Sebastian Thrun.

INVITED TALKS BY:   Leo Breiman   (Stanford, undecided)
                    Tom Mitchell  (CMU)
                    Tomaso Poggio (MIT)
                    Noel Sharkey  (Sheffield)
                    Jude Shavlik  (Wisconsin)

WEB PAGES (for more information):
Our Workshop:  http://www.cs.cmu.edu/afs/cs/usr/caruana/pub/transfer.html
NIPS*95 Info:  http://www.cs.cmu.edu/afs/cs/project/cnbc/nips/NIPS.html

WORKSHOP DESCRIPTION:
The power of tabula rasa learning is limited.  Because of this,
interest is increasing in methods that capitalize on previously
acquired domain knowledge.  Examples of these methods include:

  o  using symbolic domain theories to bias connectionist networks
  o  using unsupervised learning on a large corpus of unlabelled data
     to learn features useful for subsequent supervised learning on a
     smaller labelled corpus
  o  using models previously learned for other problems as a bias when 
     learning new, but related, problems
  o  using extra outputs on a connectionist network to bias the hidden
     layer representation towards more predictive features

There are many different approaches: hints, knowledge-based artificial
neural nets (KBANN), explanation-based neural nets (EBNN), multitask
learning (MTL), knowledge consolidation, etc.  What they all have in
common is the attempt to transfer knowledge from other sources to
benefit the current inductive task.

The goal of this workshop is to provide an opportunity for researchers
and practitioners to discuss problems and progress in knowledge
transfer in learning.  We hope to identify research directions, debate
different theories and approaches, discover unifying principles, and
begin to start answering questions like:

        o when will transfer help -- or hinder?
        o what should be transferred?
        o how should it be transferred?
        o what are the benefits?
        o in what domains is transfer most useful?

SUBMISSIONS:
We solicit presentations from anyone working in (or near):

  o  Sequential/incremental, compositional (learning by parts),
     and parallel learning
  o  Task knowledge transfer (symbolic-neural, neural-neural)
  o  Adaptation of learning algorithms based on prior learning
  o  Learning domain-specific inductive bias
  o  Combining predictions made for related tasks from one domain
  o  Combining supervised learning (where the goal is to learn one feature
     from the other features) with unsupervised learning (where the goal is
     to learn every feature from all the other features)
  o  Combining symbolic and connectionist methods via transfer
  o  Fundamental problems/issues in learning to learn
  o  Theoretical models of learning to learn
  o  Cognitive models of, or evidence for, transfer in learning

Please send a short (one page or less) description of what you want to
present to one of the co-chairs below by Oct 15.  Email is preferred.
We'll select from the submissions and publish a workshop schedule by
Nov 1.  Preference will be given to submissions that are likely to
generate debate and that go beyond summarizing prior published work by
raising important issues or suggesting directions for future work.
Suggestions for moderator or panel-led discussions (e.g., sequential
vs. parallel transfer) are also encouraged.  We plan to run the
workshop as a workshop, not as a mini conference, so be daring!  We
look forward to your submission.

   Rich Caruana                     Daniel L. Silver                
   School of Computer Science       Department of Computer Science  
   Carnegie Mellon University       Middlesex College               
   5000 Forbes Avenue               University of Western Ontario   
   Pittsburgh, PA 15213, USA        London, Ontario, Canada N6A 3K7 
   email: caruana at cs.cmu.edu        email: dsilver at csd.uwo.ca       
   ph: (412) 268-3043               ph: (519) 473-6168              
   fax: (412) 268-5576              fax: (519) 661-3515             

See you in Colorado!