CLNL'93 Schedule
Russell Greiner
greiner at learning.siemens.com
Mon Aug 9 14:59:26 EDT 1993
***********************************************************
* CLNL'93 -- Computational Learning and Natural Learning *
* Provincetown, Massachusetts *
* 10-12 September 1993 *
***********************************************************
CLNL'93 is the fourth of an ongoing series of workshops designed to bring
together researchers from a diverse set of disciplines --- including
computational learning theory, AI/machine learning,
connectionist learning, statistics, and control theory ---
to explore issues at the intersection of theoretical learning research
and natural learning systems.
The schedule of presentations appears below, followed by logistics and
information on registration
================ ** CLNL'93 Schedule (tentative) ** =======================
Thursday 9/Sept/93:
6:30-9:00 (optional) Ferry (optional): Boston to Provincetown
[departs Boston Harbor Hotel, 70 Rowes Wharf on Atlantic Avenue]
Friday 10/Sept/93 [CLNL meetings, at Provincetown Inn]
9 - 9:15 Opening remarks
9:15-10:15 Scaling Up Machine Learning: Practical and Theoretical Issues
Thomas Dietterich [Oregon State Univ]
(invited talk, see abstract below)
10:30-12:30 Paper session 1
What makes derivational analogy work: an experience report using APU
Sanjay Bhansali [Stanford]; Mehdi T. Harandi [Univ of Illinois]
Scaling Up Strategy Learning: A Study with Analogical Reasoning
Manuela M. Veloso [CMU]
Learning Hierarchies in Stochastic Domains
Leslie Pack Kaebling [Brown]
Learning an Unknown Signalling Alphabet
Edward C. Posner, Eugene R. Rodemich [CalTech/JPL]
12:30- 2 Lunch (on own)
Unscheduled TIME
( Whale watching, beach walking, ... )
( Poster set-up time; Poster preview (perhaps) )
Dinner (on own)
7 - 10 Poster Session [16 posters]
(Hors d'oeuvres)
Induction of Verb Translation Rules from Ambiguous Training and a
Large Semantic Hierarchy
Hussein Almuallim, Yasuhiro Akiba, Takefumi Yamazaki, Shigeo Kaneda
[NTT Network Information Systems Lab.]
What Cross-Validation Doesn't Say About Real-World Generalization
Gunner Blix, Gary Bradshaw, Larry Rendall [Univ of Illinois]
Efficient Learning of Regular Expressions from Approximate Examples
Alvis Brazma [Univ of Latvia]
Capturing the Dynamics of Chaotic Time Series by Neural Networks
Gurtavo Deco, Bernd Schurmann [Siemens AG]
Learning One-Dimensional Geometrical Patterns Under One-Sided Random
Misclassification Noise
Paul Goldberg [Sandia National Lab]; Sally Goldman [Washington Univ]
Adaptive Learning of Feedforward Control Using RBF Network ...
Dimitry M Gorinevsky [Univ of Toronto]
A practical approach for evaluating generalization performance
Marjorie Klenin [North Carolina State Univ]
Scaling to Domains with Many Irrelevant Features
Pat Langley, Stephanie Sage [Siemens Corporate Research]
Variable-Kernel Similarity Metric Learning
David G. Lowe [Univ British Columbia]
On-Line Training of Recurrent Neural Networks with Continuous
Topology Adaptation
Dragan Obradovic [Siemens AG]
N-Learners Problem: System of PAC Learners
Nageswara Rao, E.M. Oblow [Engineering Systems/Advanced Research]
Soft Dynamic Programming Algorithms: Convergence Proofs
Satinder P. Singh [Univ of Mass]
Integrating Background Knowledge into Incremental Concept Formation
Leon Shklar [Bell Communications Research]; Haym Hirsh [Rutgers]
Learning Metal Models
Astro Teller [Stanford]
Generalized Competitive Learning and then Handling of Irrelevant Features
Chris Thornton [Univ of Sussex]
Learning to Ignore: Psychophysics and Computational Modeling of Fast
Learning of Direction in Noisy Motion Stimuli
Lucia M. Vaina [Boston Univ], John G. Harris [Univ of Florida]
Saturday 11/Sept/93 [CLNL meetings, at Provincetown Inn]
9:00-10:00 Current Tree Research
Leo Breiman [UCBerkeley]
(invited talk, see abstract below)
10:30-12:30 Paper session 2
Initializing Neural Networks using Decision Trees
Arunava Banerjee [Rutgers]
Exploring the Decision Forest
Patrick M. Murphy, Michael Pazzani [UC Irvine]
What Do We Do When There Is Outrageous Data Points in the Data Set? -
Algorithm for Robust Neural Net Regression
Yong Liu [Brown]
A Comparison of RBF and MLP Networks for Classification of
Biomagnetic Fields
Martin F. Schlang, Ralph Neunier, Klaus Abraham-Fuchs [Siemens AG]
12:30- 2 Lunch (on own)
2:30- 3:30 TBA (invited talk)
Yann le Cun [ATT]
4:00- 6:00 Paper session 3
On Learning the Neural Network Architecture: An Average Case Analysis
Mostefa Golea [Univ of Ottawa]
Fast (Distribution Specific) Learning
Dale Schuurmans [Univ of Toronto]
Computational capacity of single neuron models
Anthony Zador [Yale Univ School of Medicine]
Probalistic Self-Structuring and Learning
A.D.M. Garvin, P.J.W. Rayner [Cambridge]
7:00- 9 Banquet dinner
Sunday 12/Sept/93 [CLNL meetings, at Provincetown Inn]
9 -11 Paper session 4
Supervised Learning from real and Discrete Incomplete Data
Zoubin Ghaharamani, Michael Jordan [MIT]
Model Building with Uncertainty in the Independent Variable
Volker Tresp, Subutai Ahmad, Ralph Neuneier [Siemens AG]
Supervised Learning using Unclassified and Classified Examples
Geoff Towell [Siemens Corp. Res.]
Learning to Classify Incomplete Examples
Dale Schuurmans [Univ of Toronto]; R. Greiner [Siemens Corp. Res.]
11:30 -12:30 TBA (invited talk)
Ron Rivest [MIT]
12:30 - 2 Lunch (on own)
3:30 - 6:30 Ferry (optional): Provincetown to Boston
Depart from Boston (on own)
------ ------
Scaling Up Machine Learning: Practical and Theoretical Issues
Thomas G. Dietterich
Oregon State University and
Arris Pharmaceutical Corporation
Supervised learning methods are being applied to an ever-expanding
range of problems. This talk will review issues arising in these
applications that require further research. The issues can be
organized according to the problem-solving task, the form of the
inputs and outputs, and any constraints or prior knowledge that must
be considered. For example, the learning task often involves
extrapolating beyond the training data in ways that are not addressed
in current theory or engineering experience. As another example, each
training example may be represented by a disjunction of feature
vectors, rather than a unique feature vector as is usually assumed.
More generally, each training example may correspond to a manifold of
feature vectors. As a third example, background knowledge may take
the form of constraints that must be satisfied by any hypothesis
output by a learning algorithm. The issues will be illustrated using
examples from several applications including recent work in
computational drug design and ecosystem modelling.
--------
Current Tree Research
Leo Breiman
Deptartment of Statistics
University of California, Berkeley
This talk will summarize current research by myself and collaborators
into methods of enhancing tree methodology. The topics covered will be:
1) Tree optimization
2) Forming features
3) Regularizing trees
4) Multiple response trees
5) Hyperplane trees
These research areas are in a simmer. They have been programmed and
are undergoing testing. The results are diverse.
--------
--------
Programme Committee:
Andrew Barron, Russell Greiner, Tom Hancock, Steve Hanson, Robert Holte,
Michael Jordan, Stephen Judd, Pat Langley, Thomas Petsche, Tomaso Poggio,
Ron Rivest, Eduardo Sontag, Steve Whitehead
Workshop Sponsors:
Siemens Corporate Research and MIT Laboratory of Computer Science
================ ** CLNL'93 Logistics ** =======================
Dates:
The workshop begins at 9am Friday 10/Sept, and concludes by 3pm
Sunday 12/Sept, in time to catch the 3:30pm Provincetown--Boston ferry.
Location:
All sessions will take place in the Provincetown Inn (800 942-5388); we
encourage registrants to stay there. Provincetown Massachusetts is located
at the very tip of Cape Cod, jutting into the Atlantic Ocean.
Transportation:
We have rented a ship from The Portuguese Princess to transport CLNL'93
registrants from Boston to Provincetown on Thursday 9/Sept/93, at no charge
to the registrants. We will also supply light munchies en route. This ship
will depart from the back of Boston Harbor Hotel, 70 Rowes Wharf on Atlantic
Avenue (parking garage is 617 439-0328); tentatively at 6:30pm.
If you are interested in using this service, please let us know ASAP (via
e-mail to clnl93 at learning.scr.siemens.com) and also tell us whether you be
able to make the scheduled 6:30pm departure.
(N.b., this service replaces the earlier proposal, which involved the
Bay State Cruise Lines.)
The drive from Boston to Provincetown requires approximately two hours.
There are cabs, busses, ferries and commuter airplanes (CapeAir, 800 352-0714)
that service this Boston--Provincetown route.
The Hyannis/Plymouth bus (508 746-0378) leaves Logan Airport at 8:45am,
11:45am, 2:45pm, 4:45pm on weekdays, and arrives in Provincetown about
4 hours later; its cost is $24.25.
For the return trip (only), Bay State Cruise Lines (617 723-7800) runs a
ferry that departs Provincetown at 3:30pm on Sundays, arriving at
Commonwealth Pier in Boston Harbor at 6:30pm; its cost is $15/person, one way.
Inquiries:
For additional information about CLNL'93, contact
clnl93 at learning.scr.siemens.com
or
CLNL'93 Workshop
Learning Systems Department
Siemens Corporate Research
755 College Road East
Princeton, NJ 08540--6632
To learn more about Provincetown, contact their
Chamber of Commerce at 508 487-3424.
================ ** CLNL'93 Registration ** =======================
Name: ________________________________________________
Affiliation: ________________________________________________
Address: ________________________________________________
________________________________________________
Telephone: ____________________ E-mail: ____________________
Select the appropriate options and fees:
Workshop registration fee ($50 regular; $25 student) ___________
Includes
* attendance at all presentation and poster sessions
* the banquet dinner on Saturday night; and
* a copy of the accepted abstracts.
Hotel room ($74 = 1 night deposit) ___________
[This is at the Provincetown Inn, assuming a minimum stay of
2 nights. The total cost for three nights is $222 = $74 x 3,
plus optional breakfasts.
Room reservations are accepted subject to availability.
See hotel for cancellation policy.]
Arrival date ___________ Departure date _____________
Name of person sharing room (optional) __________________
[Notice the $74/night does correspond to $37/person per
night double-occupancy, if two people share one room.]
# of breakfasts desired ($7.50/bkfst; no deposit req'd) ___
Total amount enclosed: ___________
If you are not using a credit card, make your check payable in U.S. dollars
to "Provincetown Inn/CLNL'93", and mail your completed registration form to
Provincetown Inn/CLNL
P.O. Box 619
Provincetown, MA 02657.
If you are using Visa or MasterCard, please fill out the following,
which you may mail to above address, or FAX to 508 487-2911.
Signature: ______________________________________________
Visa/MasterCard #: ______________________________________________
Expiration: ______________________________________________
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