ICML-97 workshops CFPs
gordon@AIC.NRL.Navy.Mil
gordon at AIC.NRL.Navy.Mil
Wed Jan 29 14:41:16 EST 1997
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CALL FOR PAPERS
REINFORCEMENT LEARNING: TO MODEL OR
NOT TO MODEL, THAT IS THE QUESTION
Workshop at the Fourteenth
International Conference on Machine
Learning (ICML-97)
Nashville, Tennessee
July 12, 1997
Recently there has been some disagreement in the reinforcement
learning community about whether finding a good control policy
is helped or hindered by learning a model of the system to be
controlled. Recent reinforcement learning successes
(Tesauro's TD-gammon, Crites' elevator control, Zhang and
Dietterich's space-shuttle scheduling) have all been in
domains where a human-specified model of the target system was
known in advance, and have all made substantial use of the
model. On the other hand, there have been real robot systems
which learned tasks either by model-free methods or via
learned models. The debate has been exacerbated by the lack
of fully-satisfactory algorithms on either side for
comparison.
Topics for discussion include (but are not limited to)
o Case studies in which a learned model either contributed to
or detracted from the solution of a control problem. In
particular, does one method have better data efficiency?
Time efficiency? Space requirements? Final control
performance? Scaling behavior?
o Computational techniques for finding a good policy, given a
model from a particular class -- that is, what are good
planning algorithms for each class of models?
o Approximation results of the form: if the real system is in
class A, and we approximate it by a model from class B, we
are guaranteed to get "good" results as long as we have
"sufficient" data.
o Equivalences between techniques of the two sorts: for
example, if we learn a policy of type A by direct method B,
it is equivalent to learning a model of type C and computing
its optimal controller.
o How to take advantage of uncertainty estimates in a learned
model.
o Direct algorithms combine their knowledge of the dynamics and
the goals into a single object, the policy. Thus, they may
have more difficulty than indirect methods if the goals change
(the "lifelong learning" question). Is this an essential
difficulty?
o Does the need for an online or incremental algorithm interact
with the choice of direct or indirect methods?
There will be presentations at the workshop by both invited
speakers and authors of accepted papers; in addition, we may
schedule a poster session after the workshop. Contributions
that argue a position, give an overview or review, or report
recent work are all encouraged.
3 hardcopies of extended abstracts or full papers papers no
longer than 15 pages should be sent to arrive by March 15th,
1997 to Geoff Gordon (address below). Please also email a URL
that points to your submission to ggordon at cs.cmu.edu by the
same date.
Accepted papers will be included in the hardcopy workshop
proceedings (the ICML-97 style file will be available for
final formatting). The URLs will be used to create an
electronic proceedings. We would like the electronic
proceedings to contain online copies of slides, posters, etc.
in addition to the papers.
Important Dates:
March 15, 1997: Extended abstracts and papers due
April 10, 1997: Notification of acceptance
May 1, 1997: Camera-ready copy of papers due
July 12, 1997: Workshop
Organizers:
Chris Atkeson (cga at cc.gatech.edu)
College of Computing
Georgia Institute of Technology
801 Atlantic Drive
Atlanta, GA 30332-0280
Geoff Gordon (ggordon at cs.cmu.edu)
Computer Science Department
Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15213-3891
(412) 268-3613, (412) 361-2893
Contact:
Geoff Gordon (ggordon at cs.cmu.edu)
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CALL FOR PAPERS
AUTOMATA INDUCTION, GRAMMATICAL INFERENCE, AND LANGUAGE ACQUISITION
Workshop at the Fourteenth
International Conference on Machine
Learning (ICML-97)
Nashville, Tennessee
July 12, 1997
The Automata Induction, Grammatical Inference, and Language Acquisition
Workshop will be held on Saturday, July 12, 1997 during the Fourteenth
International Conference on Machine Learning (ICML-97) which will be
co-located with the Tenth Annual Conference on Computational Learning Theory
(COLT-97) at Nashville, Tennessee from July 8 through July 12, 1997.
Additional information on ICML-97 and COLT-97 can be found at:
http://cswww.vuse.vanderbilt.edu/~mlccolt/
Objectives
Machine learning of grammars, variously referred to as automata induction,
grammatical inference, grammar induction, and automatic language acquisition,
finds a variety of applications in syntactic pattern recognition,
adaptive intelligent agents, diagnosis, computational biology,
systems modelling, prediction, natural language acquisition,
data mining and knowledge discovery.
The workshop seeks to bring together researchers working on
different aspects of machine learning of grammars in a number
of different (and until now, relatively isolated) areas including
neural networks, pattern recognition, computational linguistics,
computational learning theory, automata theory, and language acquisition
for fruitful exchange of the relevant recent research results.
Workshop Format
The workshop will consist of 3--5 invited talks offering different
perspectives on machine learning of grammars, interspersed with
short (10--15 minute) presentations of accepted papers. The workshop
schedule will allow ample time for informal discussion.
Topics of Interest
Topics of interest include, but are not limited to:
Different models of grammar induction:
e.g., learning from examples,
learning using examples and queries,
incremental versus non-incremental learning,
distribution-free models of learning,
learning under various distributional assumptions
(e.g., simple distributions).
Theoretical results in grammar induction:
e.g., impossibility results,
complexity results,
characterizations of representational and search
biases of grammar induction algorithms.
Algorithms for induction of different classes of languages and
automata:
e.g., regular,
context-free, and
context-sensitive languages,
interesting subsets of the above under additional
syntactic constraints, tree and graph grammars,
picture grammars, multi-dimensional grammars,
attributed grammars, etc.
Empirical comparison of different approaches to grammar induction.
Demonstrated or potential applications of grammar induction in
natural language acquisition,
computational biology,
structural pattern recognition,
adaptive intelligent agents,
systems modelling,
and other domains.
Submission Guidelines
Full paper submissions are highly recommended although
extended abstracts will also be considered.
The manuscript should be no more than 10 pages
long when formatted for generic 8-1/2 x 11 inch pages using the
formatting macros and templates available at:
http://www.aaai.org/Publications/Templates/macros-link.html
Postscript versions of the manuscripts should be emailed
so as to arrive by March 15, 1997 at:
honavar at cs.iastate.edu, pdupont at cs.cmu.edu, giles at research.nj.nec.com.
Deadlines
Deadline for submission of manuscripts: March 15, 1997
Decisions regarding acceptance or rejection emailed to authors: April 1, 1997
Final versions of the papers due: April 15, 1997
Selection Criteria
Selection of submitted papers will be on the basis of review
by at least two referees. Review criteria include: originality, technical
soundness, clarity of presentation, relevance of the results and
potential appeal to the workshop audience.
Workshop Proceedings
Workshop proceedings will be published in electronic form on the world-wide
web. Authors of a selected subset of accepted workshop papers might also be
invited to submit revised and expanded versions of their papers for possible
publication in a special issue of a journal or an edited collection of papers
to be published after the conference.
Workshop Organizers:
Dr. Vasant Honavar
Department of Computer Science
226 Atanasoff Hall
Iowa State University
Ames, IA 50011
honavar at cs.iastate.edu
Dr. Pierre Dupont
Department of Computer Science
Carnegie Mellon University
5000 Forbes Ave
Pittsburgh, PA 15213
pdupont at cs.cmu.edu
Dr. Lee Giles
NEC Research Institute
4 Independence Way
Princeton, NJ 08540
giles at research.nj.nec.com
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CALL FOR PAPERS
ML APPLICATION IN THE REAL WORLD:
METHODOLOGICAL ASPECTS AND IMPLICATIONS
Workshop at the Fourteenth
International Conference on Machine
Learning (ICML-97)
Nashville, Tennessee
July 12, 1997
WWW-page: http://www.aifb.uni-karlsruhe.de/WBS/ICML97/ICML97.html
Description
Application of Machine Learning techniques to solve real-world problems
has gained more and more interest over the last decade. In spite of this
attention, the ML application process is still lacking a generally accepted
terminology, let alone commonly accepted approaches or solutions.
Several initiatives, both conferences and workshops have been held
concerning this topic.
The ICML-93 workshop of Langley and Kodratoff on ML applications as well
as at the ICML-95 workshop on 'Applying Machine Learning in Practice' by
Aha, Catlett, Hirsh and Riddle form the successful precedents of this workshop.
The focus of the ICML-95 workshop was the 'characterization of the
expertise used by machine learning experts during the course of applying
learning algorithms to practical applications'. In the last year a
significant research effort has been spent that deals with applications
of learning algorithms. A reflection of this is the recent interest in
Data Mining and KDD, as for instance reflected in the international KDD-
conference (1995 (Montreal) and 1996 (Portland, OR)). Since the
application of ML-techniques is also very relevant to the KDD-community
it is not surprising that this is also reflected in those conferences.
The workshop will draw along the lines of all these events, but
will emphasise the processes underlying the application of ML in
practice. Methodological issues, as well as issues concerning the kinds
and roles of knowledge needed for applying ML will form a major focus
of the workshop.
It aims at building upon some of the results of discussions at the
ICML-95 workshop on "Application of ML techniques in practice"
and at the same time tries to move forward to a consensus regarding a
methodology on the application of learning algorithms in practice.
The workshop "ML Application in the real world; methodological aspects and
implications" focuses on the methodological principles underlying
successful application of ML techniques. Apart from powerful ML
algorithms, good application strategies have to be defined. This implies a
thorough understanding of the initial problem definition and its relation
to the chain of tasks that leads towards a successful solution. Therefore a
two-dimensional approach regarding the process of ML application is
needed. The first dimension deals with the whole cycle of analysing the
setting, problem definition, knowledge extraction, database interaction,
learning, evaluation and iteration in real-world domains, where the second
dimension forms an "inner loop" to this cycle, where the problem
definition is used to refine the task at hand and map it on available
algorithms for learning, pre- and postprocessing and evaluation of
results.
Concerning these issues there is no clear distinction between ML and KDD,
and therefore this workshop will be equally interesting for
researchers from both communities.
This workshop does not focus on (methods for) developing new algorithms.
Moreover, case studies will only contribute to the workshop discussion if
general application principles can be derived from them.
Intended Participants and Audience
The workshop primarily aims at scientists and practitioners that apply ML
and related techniques to solve problems in the real world. To attend
the workshop, one should submit a paper, a one page extended abstract or
a statement of interest. In case of too much interest from
participants, the program committee will select participants on the
basis of workshop relevance. Ideally, the audience contains a mix of
university and industrial participants.
Workshop program
The program for this one-day workshop will have a maximum of 10
presentations. Some invited presentations will be part of the program.
Presentations will take 30 minutes (15-20 minutes presentation and 10-15
minutes discussion). Speakers are asked to focus their presentation on
the basis of a topic list that will be compiled during the review
process. To foster discussion and debate, accepted papers will be given
to a critic beforehand; by these means critics will be prepared to
debate presentations. At the end of the workshop, there will be a
plenary discussion session. Accepted papers will be distributed via the
workshop WWW-page before the workshop, to stimulate the discussion.
Accepted papers will also be published in workshop proceedings.
Papers are welcomed concerning (but not limited to) the following
topics:
* Methodological approaches focusing on the process of ML application,
or sub-processes, such as problem definition and refinement,
application design, data acquisition, pre- and postprocessing, task
analysis etc.
* Making explicit the kinds and roles of knowledge that are necessary
for execution of ML applications.
* Matching of problem definitions on specific techniques and multi-
technique configurations.
* Impact of methodologies for empirical research on the application of
ML-techniques.
* Identification of the relation of different ML strategies to given
problem types and identification of the characteristics that play a
role in describing the initial problems.
* Embedding of the ML application process in more general methodologies
for (knowledge) system development.
* Frameworks for support of (ML-)novices and experts for setting up
applications and reuse of previously application(part)s.
* Case studies, describing successful ML applications, that abstract
from the implementational aspects and focus on identification of the
choices that are made when designing the application i.e. the
(meta-)knowledge involved, etc.
* Comparison of the process of ML application with processes for
application of related techniques (e.g. statistical data analysis).
Submission guidelines
* Submitted papers should not exceed 3500 words or 8 pages Times Roman
12pt.
* The title page should contain paper title, author name(s), affiliations and
full addresses including e-mail of the corresponding author, as well as the
paper abstract and five keywords at most.
* Papers are reviewed by at least three members of the program committee on
their relevance for the workshop discussions.
* For preparation of the camera ready copies, an ICML style file will be
available.
Tentative Submission Schedule
* Submission deadline: March 22, 1997
* Notification of acceptance: April 9, 1997
* Camera ready copy + PS-file: May 1, 1997
* Papers available on WWW: June 15, 1997
* Workshop date: July 12, 1997
Electronic paper submissions are preferred. Please send your submission
to:
MLApplic.ICML at ato.dlo.nl.
If Postscript printing is not available, paper submissions (4 hardcopies,
preferably double sided) can be sent to:
ICML Workshop "ML APPLICATION IN THE REAL WORLD"
p/o ATO-DLO, Floor Verdenius
Postbus 17
6700 AA Wageningen
Netherlands
Program Committee
Dr. Pieter Adriaans (Syllogic, Houten, The Netherlands)
Prof. C. Brodley (Purdue University, West Lafayette, IND, USA)
Prof. David Hand (Open University, Milton Keynes, United Kingdom)
Prof. Yves Kodratoff (LRI, Paris, France)
Dr. Vassilis Moustakis (Technical University of Crete, Chania, Greece)
Prof. Gholamreza Nakhaeizadeh (Daimler Benz AG Research, Ulm, Germany)
Dr. R. Kohavi (Silicon Graphics, Mountain View, CA, USA)
Dr. Enric Plaza i Cervera (IIIA-CSIC, Bellaterra, Catalonia, Spain)
Dr. Foster J. Provost (NYNEX Science & Technology, White Plains, NY,
USA)
Dr. P. Riddle (University of Auckland, New Zealand)
Dr. Celine Rouveirol (LRI, Paris, France)
Prof. Derek Sleeman (University of Aberdeen, United Kingdom)
Drs. Maarten van Someren (SWI, Amsterdam, The Netherlands)
Prof. Rudi Studer (University of Karlsruhe, Germany)
Organising Committee
Robert Engels (University of Karlsruhe, Germany)
engels at aifb.uni-karlsruhe.de
Juergen Herrmann (University of Dortmund, Germany)
Herrmann at jupiter.informatik.uni-dortmund.de
Bob Evans (RR Donnelley, Gallatin TN, USA)
BOB.EVANS at rrd.com
Floor Verdenius (ATO-DLO, Wageningen, The Netherlands)
F.Verdenius at ato.dlo.nl
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