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From: dfisher@vuse.vanderbilt.edu (Douglas H. Fisher)
Subject: AI/Stats
Message-ID: <1994Oct1.144711.29786@news.vanderbilt.edu>
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Organization: Department of Computer Science, Vanderbilt University, Nashville, TN, USA
Date: Sat, 1 Oct 1994 14:47:11 GMT
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              Preliminary Call for Participation

               Fifth International Workshop on
           ARTIFICIAL INTELLIGENCE and STATISTICS

                    January 4-7, 1995
                 Ft. Lauderdale, Florida


TECHNICAL and TUTORIAL PROGRAM:
This is the fifth in a series of workshops that has brought 
together researchers in Artificial Intelligence and in
Statistics to discuss problems of mutual interest. To 
encourage interaction and a broad exchange of ideas, there
will be 20 discussion papers in single session meetings over 
three days (Jan. 5-7). Two poster sessions will provide the 
means for presenting and discussing the remaining research 
papers. Attendance at the workshop is *not* limited to paper 
presenters.

The three days of research presentations will be preceded by 
a day of tutorials (Jan. 4). The tutorial topics, presenters, 
and approximate times are:

 (1) Machine Learning                         9:00AM - 12:15PM
     (Dr. David Aha, Naval Research Lab)                

 (2) Statistical Methods for Inducing         9:00AM - 12:15PM   
        Models from Data   
     (Prof. Steffen Lauritzen, Aalborg U.)

 (3) Probabilistic Models of Causality        2:00PM - 5:15PM  
     (Prof. Glenn Shafer, Rutgers U.)

 (4) Statistical Models for Function          2:00PM - 5:15PM
        Estimation and Classification
     (Prof. Trevor Hastie, Stanford U.)

Notes prepared by the tutorial presenters will be made available
at the Workshop. 

LOCATION:
The 1995 Workshop will be held at 

           Pier Sixty Six Resort & Marina 
           2301 SE 17th Street Causeway
           Fort Lauderdale, Florida, 33316
           USA.

           Phone: 800-327-3796 (outside Florida)
                  305-525-6666
           Fax  : 305-728-3541

The hotel is a 22 acre resort located on the intracoastal waterway.
Available amenities include two pools, a 40 person hydrotherapy 
pool, spa, tennis courts, a children's activity club, seven 
restaurants and lounges, and water shuttle service to the beach. 

The Hotel is most conveniently reached from Fort Lauderdale 
International Airport, which is about 5-10 minutes by car/cab.
The Hotel is approximately 45-60 minutes by car from Miami 
International Airport.

The Resort is holding a block of rooms at the rate of $95 US 
dollars (for single/double) until Dec. 10, 1994.  Reservations 
should be made before this date. The block is held under the 
name `SOCIETY for ARTIficial Intelligence and Statistics' 
(or SOCIETY ARTI).


REGISTRATION:
Registration for the Technical Program (plenary and poster
sessions) includes a proceedings of papers submitted by authors,
continental breakfasts each day of the technical program, 
and tentatively, two lunches and one dinner. The Workshop 
offers student rates and an early-registration discount. 
Registration rates and instructions can be found on the 
Registration Form at the end of this Call. Registration 
for tutorials can also be made in advance using the 
Registration Form.


PROGRAM COMMITTEE:

General Chair:    D. Fisher             Vanderbilt U., USA
Program Chair:    H. Lenz               Free U. Berlin, Germany
Members:          W. Buntine            NASA (Ames), USA
                  J. Catlett            AT&T Bell Labs, USA
                  P. Cheeseman          NASA (Ames), USA
                  P. Cohen              U. of Mass., USA 
                  D. Draper             U. of Bath, UK
                  Wm. Dumouchel         Columbia U., USA
                  A. Gammerman          U. of London, UK
                  D. J. Hand            Open U., UK
                  P. Hietala            U. Tampere, Finland
                  R. Kruse              TU Braunschweig, Germany
                  S. Lauritzen          Aalborg U., Denmark
                  W. Oldford            U. of Waterloo, Canada
                  J. Pearl              UCLA, USA
                  D. Pregibon           AT&T Bell Labs, USA
                  E. Roedel             Humboldt U., Germany
                  G. Shafer             Rutgers U., USA
                  P. Smyth              JPL, USA
Tutorial Chair:   P. Shenoy             U. Kansas, USA


MORE INFORMATION:
For more information write dfisher@vuse.vanderbilt.edu
or call 615-343-4111.


SPONSORS: Society for Artificial Intelligence and Statistics
          International Association for Statistical Computing


                            ***********


                 Papers accepted for Technical Program

                    Fifth International Workshop on
                       Artificial Intelligence
                                and
                             Statistics



                          PLENARY PAPERS


Almond, Schimert (MathSoft)    Missing data models as meta-data 

Brent, Murthy, Lundberg        Minimum description length induction
       (John Hopkins U)           for discovering morphemic suffixes

Buntine (NASA Ames)            Software for data analysis with
                                  graphical models: basic tools

Chickering, Geiger, Heckerman  Learning Bayesian networks: search
       (MicroSoft)                methods and experimental results

Cohen, Gregory, Ballesteros,   Two algorithms for inducing structural
       St Amant (U Mass)          equation models from data

Cooper (U Pitt)                Causal discovery from observational
                                  data in the presence of selection
                                  bias

Cox (US West)                  Using causal knowledge to learn more
                                  useful decision rules from data 

Decatur (Harvard U)            Learning in hybrid noise environments
                                  using statistical queries

Elder (Rice U)                 Heuristic search for model structure

Gebhardt, Kruse                Learning possibilistic networks from data
    (U Braunschweig)                   

Kasahara, Ishikawa,            Viewpoint-based measurement of semantic
      Matsuzawa, Kawaoka          similarity between words 
      (Nippon TT)                  

Lubinsky (U Witwatersrand SA)  Structured interpretable regression

Madigan, Almond (U Washington) Test selection strategies for belief
                                  networks

Malvestuto (U L'Aquila, IT)    Derivation DAGs for inferring
                                  interaction models

Merz (U Cal Irvine)            Dynamic learning bias selection 

Pearl (UCLA)                   A causal calculus for statistical
                                  research with applications to
                                  observational and experimental
                                  studies

Riddle, Frenedo, Newman        Framework for a generic knowledge
       (Boeing)                   discovery tool 

Shafer, Kogan, Spirtes         A generalization of the Tetrad
        (Rutgers)                  representation theorem
                                       
St Amant, Cohen (U Mass)       Preliminary design for an EDA assistant

Yao, Tritchler (U Toronto)     Likelihood-based causal inference




                     POSTER PAPERS

        
Aha, Bankert (NRL)             A comparative evaluation of
                                  sequential feature selection
                                  algorithms

Ali, Brunk, Pazzani            Learning multiple relational rule-based
      (U Cal Irvine)              models

Almond (MathSoft)              Hypergraph grammars for knowledge-based
                                  model construction 

Anderson, Carlson, Westbrook   Tools for analyzing AI programs
      Hart, Cohen (U Mass)         

Bergman, Rivest (MIT)          Picking the best expert from a sequence

Blau (U Rochester)             Ploxoma: Test-bed for uncertain
                                  inference 

Breese, Heckerman              Probabilistic case-based reasoning
       (MicroSoft)                   

Burke (U Nevada)               Comparing the prediction accuracy of
                                  statistical models and artificial
                                  neural networks in breast cancer

Catlett (ATT)                  Tailoring rulesets to misclassification
                                  cost

Chen, Yeh                      Predicting stock returns with genetic
      (National Chengchi U)       programming 

Cheng (U Cincinnati)           Analysis and Application of the
                                  Generalized Mean-Shift Process

Cozman, Krotkov (CMU)          Truncated Gaussians as tolerance sets

Cunningham (U Waikato)         Textual data mining

De Vel, Li, Coomans            Non-Linear dimensionality reduction:
      (U James Cook, NZ)          A comparative performance study 


DuMouchel, Friedman, Johnson   Natural language processing of
      Hripcsak (Columbia U)       radiology reports 

Esposito, Malerba, Semeraro    A further study of pruning methods in
      (U degli Studi, IT)         decision tree induction

Feelders, Verkooijen           Which method learns most from the data?
       (U Twente, Netherlands)

Franz (CMU)                    Classifying new words for robust
                                  parsing

Gelsema (Erasmus U,            Abductive reasoning in Bayesian belief 
       The Netherlands)           networks using a genetic algorithm

Harner, Galfalvy               Omega-Stat: An environment for 
        (West Virginia U)         implementing intelligent modeling 
                                  strategies 

Heckerman, Shachter            A decision-based view of causality
       (MicroSoft)

Howe (Colorado St U)           Finding dependencies in event streams
                                  using local search

Jenzarli (U Tampa)             Solving influence diagrams using
                                  Gibbs sampling 

John (Stanford U)              Robust linear discriminant trees

Ketterlin, Gancarski, Korczak  Hierarchical clustering of composite
       (U Louis Pasteur)          objects with a variable number of
                                  components

Kim (Korea Adv. Inst. of Sci.  An approach to fitting large influence
      and Eng.)                   diagrams

Kim, Moon (Syracuse U)         Modeling life time data by neural
                                  networks 

Kloesgen (German Nat. Rsch.)   Learning from data: Pattern evaluations
                                  and search strategies

Larranaga, Murga, Poza,        Structure learning of Bayesian networks 
       Kuijpers (U Basque,        by hybrid genetic algorithms
       Spain)

Lekuona, Lacruz, Lasala        Graphical models for dynamic systems
       (U de Zaragoza, Spain)

Liu (U Kansas)                 Propagation of Gaussian belief
                                  functions 

Martin (U Cal, Irvine)         A hypergeometric null hypothesis
                                  probability test for feature
                                     selection and stopping 

Martin (U Cal, Irvine)         Evaluating and comparing classifiers:
                                  Complexity measures

Murthy (John Hopkins U)        Statistical preprocessing of
                                  decision trees

Neufeld, Adams, Choy, Philip,  Part-of-speech tagging from small
        Tawfik (U Saskatchewan)   data sets

Oates, Gregory, Cohen (U Mass) Detecting complex dependencies in
                                  categorical data

Pazzani (U Cal Irvine)         Searching for attribute dependencies
                                  in Bayesian classifiers

Provan, Singh (Inst. for       Learning ``Predictively-Optimal''
      Decision Systems Res.)      Bayesian Networks 

Risius, Seidelmann             Combining statistics and AI in the
      (Hahn-Meitner Inst)         optimization of semiconductor films
                                     for solar cells

Shenoy (U Kansas)              Representing and solving asymmetric
                                  decision problems using valuation
                                  networks

Srkantan, Srihari              Data representations in learning
       (SUNY Buffalo) 

Sun, Qiu, Cox (US West)        A hill-climbing approach to construct
                                  near optimal decision trees

Valtorta (U South Carolina)    MENTOR: A Bayesian model for prediction
                                  and intervention in mental
                                  retardation 

Young, Lubinsky (UNC)          Learning from data by guiding the
                                  analyst: On the representation, use,
                                  and creation of visual statistical
                                  strategies 


                            ***********


                         Registration Form

                    Fifth International Workshop on
                       Artificial Intelligence
                                and
                             Statistics
            

Participants may register on site. To register in advance of 
the Workshop send this form and a check (in US dollars) made 
to the order of **Society for Artificial Intelligence and 
Statistics** in the appropriate amount to:

    Doug Fisher
    Department of Computer Science
    Box 1679, Station B
    Vanderbilt University
    Nashville, Tennessee  37235
    USA

Advance registration discounts apply if registration is received
by Dec. 1, 1994.


Name: ________________________________________

Affiliation: _________________________________

Phone: _______________________________________

Fax: _________________________________________

Email: _______________________________________

Address: _____________________________________

         _____________________________________

         _____________________________________


Technical Program -- check one:

  ____   Technical Program (regular, by Dec. 1, 1994):        $245
  
  ____   Technical Program (student, by Dec. 1, 1994):        $155

  ____   Technical Program (regular, after Dec. 1, 1994):     $295
   
  ____   Technical Program (student, after Dec. 1, 1994):     $195


Technical Program Subtotal:                                   $____ 


Tutorial Program -- check applicable tutorials, if any.
                    Note that the tutorial times may conflict;
                    to avoid conflict at most one selection
                    from (1) and (2), and one selection from
                    (3) and (4) may be made.


  ____  (1) Machine Learning  

                    ____ (regular, by Dec. 1):                $ 70

                    ____ (student, by Dec. 1):                $ 45

                    ____ (regular, after Dec. 1):             $ 80

                    ____ (student, after Dec. 1):             $ 55


  ____  (2) Statistical Methods for Inducing Models from Data

                    ____ (regular, by Dec. 1):                $ 70

                    ____ (student, by Dec. 1):                $ 45

                    ____ (regular, after Dec. 1):             $ 80

                    ____ (student, after Dec. 1):             $ 55


  ____  (3) Probabilistic Models of Causality

                    ____ (regular, by Dec. 1):                $ 70

                    ____ (student, by Dec. 1):                $ 45

                    ____ (regular, after Dec. 1):             $ 80

                    ____ (student, after Dec. 1):             $ 55


  ____  (4) Statistical Models for Function Estimation and Classification 

                    ____ (regular, by Dec. 1):                $ 70

                    ____ (student, by Dec. 1):                $ 45

                    ____ (regular, after Dec. 1):             $ 80

                    ____ (student, after Dec. 1):             $ 55


Tutorial Program Subtotal:                                    $____


Technical and Tutorial Total:                                 $____



                            ***********




