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      First Announcement/Call for Papers and Participation
      ----------------------------------------------------
Attribute-Value and Relational Learning: Crossing the Boundaries
================================================================
     A Workshop at the Seventeenth International Conference
                on Machine Learning (ICML-2000)
                      Stanford University
                     June 29 - July 2, 2000

http://www.informatik.uni-freiburg.de/~ml/icml2000_workshop.html

The purpose of this workshop is to investigate the boundaries between
learning with attribute-value representations and relational ones.
There are several reasons for being interested in these boundaries.

In recent years, people from the attribute-value learning community
have used richer representations for learning to tackle practical
problems that are hard to represent within the attribute-value
formalism. One example of such work concerns multi-instance learning,
in which each example corresponds to a set of tuples in a single
relation. This formulation is more expressive than the usual
attribute-value setting, which requires each example to be a single
tuple, but less expressive than the typical relational setting, which
allows for multiple relations as well.

Another line of research working toward the boundaries concerns
propositionalization in inductive logic programming. Various
researchers in this area have proposed ways to derive propositional
features from relational problems and then used these features
successfully in attribute-value learners.

Boundaries between attribute-value and relational learning have not
only been crossed in traditional symbolic machine learning but also in
areas such as probabilistic reasoning, case-based reasoning, and even
reinforcement learning. For instance, some work has extended methods
for learning in Bayesian networks to handle relational representations,
and methods for analogical reasoning often employ a first-order or
relational representation.

As in the knowledge representation community, many researcers in
machine learning and data mining are concerned with the boundaries
between relational and propositional representations. The issue
under investigation is often the trade-off between complexity of
the algorithms and expressiveness of the representation languages.

This workshop hopes to present recent research results in this
area, to make progress on understanding the boundaries, to bring
together researchers from different communities, and to stimulate
fruitful discussions among participants.

A list of topics of interest includes (but is not limited to):

  * multi-instance learning

  * propositionalization of relational formalisms

  * using intermediate representations for learning (e.g., some
    description logics)

  * frameworks that capture the relation among attribute-value and
    relational learning

  * empirical and/or theoretical results on the trade-off between
    complexity of algorithms and expressiveness of representations

  * extensions of probabilistic and case-based learning to support
    relational formalisms

  * background knowledge in attribute-value and relational learning

  * hybrid approaches that merge the two approaches to learning

  * extensions of attribute-value learning and specialisations of
    relational learning to handle sequential and hierarchical data

  * neural network learning with structured data

Workshop Organization and Submission Requirements
-------------------------------------------------

The workshop will feature a number of invited presentations by
people who have contributed to the area. A preliminary list of
invited speakers includes:

  * Lorenza Saitta (Universita di Torino, Italy)
  * Michele Sebag (Ecole Polytechnique, Palaiseau, France)
  * Ashwin Srinivasan (University of Oxford, UK)
  * Mark Craven (University of Wisconsin, USA)
  * Daphne Koller (Stanford University, USA)
  * Russ Greiner (University of Alberta, Canada)

Researchers wishing to present their own results at the workshop
should submit an extended abstract, no longer than 2000 words, to
skramer@informatik.uni-freiburg.de, deraedt@informatik.uni-freiburg.de,
subo@informatik.uni-freiburg.de, preferably in HTML, PDF or Postscript.
The abstracts will be put on the Web before the workshop. Submissions
will be judged mainly on their relevance to the workshop topic, i.e.,
they should make explicit their contribution to the exploration of the
boundaries between attribute-value and relational learning. Abstracts
that focus solely on either side of the boundary will not be accepted
for presentation.

To guarantee a true workshop atmosphere, the workshop will be restricted
to 50 participants. Researchers interested in participating should send
an email (including address and email) to
subo@informatik.uni-freiburg.de
to register  for the workshop. If more than 50 persons are interested in
participating in the workshop,  participants will be selected on a
first-come first-serve basis.

Important Dates
---------------

Submission deadline: April 17, 2000
Notification of acceptance: May 15, 2000.
Workshop held: between June 29 and July 2, 2000
               (to be announced early March)

Chairs
------

Luc De Raedt (Albert-Ludwigs-University, Freiburg, Germany)
Stefan Kramer (Albert-Ludwigs-University, Freiburg, Germany)

Organizing Committee
--------------------

Luc Dehaspe (Katholieke Universiteit Leuven, Belgium)
Saso Dzeroski (Jozef Stefan Institute, Ljubljana, Slovenia)
Roni Khardon (Univ. of Edinburgh, UK)
Bernhard Pfahringer (University of Waikato, New Zealand)
Lorenza Saitta (Universita di Torino, Italy)
Michele Sebag (Ecole Polytechnique, Palaiseau, France)
Ashwin Srinivasan (University of Oxford, UK)
Hannu Toivonen (Nokia Research Center, Finland)
Mark Craven (University of Wisconsin)
Oded Maron (PHZ Capital Partners)
Daphne Koller (Stanford University)
Russ Greiner (University of Alberta, Canada)

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