Boulder Computational Learning Group
Spring 1998 Schedule
Unless otherwise noted, meeting time is on Mondays, 1-3 p.m.,
in ECOT 831a (Engineering center, 8th floor, small conference
room). Dates in red are at the usual time and place.
Dates in blue are special meeting dates.
Schedules from previous semesters
January 12: No Meeting (first day of classes)
January 19: No Meeting (holiday)
January 26: Satinder Singh
Recent research on reinforcement learning
February 2: Tor Mohling
A computational model of unconscious and conscious processes in human
anagram solving
February 9: Marc Pickett
February 16: NO MEETING (President's day)
March 2: Manav Misra
Graph matching for visual pattern recognition
March 6: Larry Reeker, NSF (tentative)
March 9: Robert Dodier
Belief nets
March 19, 3:45 p.m., ECCR 265: Yoram Singer, AT&T
``80 and tea: ho May I hell Pooh ?'':
Efficient algorithms for multi-class multi-label text categorization
When an At&T customer dials 00, she gets an operator who asks: ``AT&T: how
may I help you?''. A possible answer might be: ``Ah yes, I would like to
call 908-508-1464 and charge it to my Visa.'' Being able to automatically
determine what the caller actually wants (in this case a `dial-for-me'
request and a `credit-card-call' request) is a difficult task especially
since the might be more than one action to be taken due to the caller's
request.
This work focuses on algorithms which learn from examples to perform
text and speech categorization tasks. We first show how to extend the
standard notion of classification by allowing each instance to be
associated with multiple labels. We then discuss our approach for
text classification which is based on a new and improved family of
boosting algorithms. We describe in detail an implementation, called
BoosTexter, of the new boosting algorithms for text classification
tasks. We present results comparing BoosTexter and a number of other
text classification algorithms. We conclude with a demonstration of
the system for the ``AT&T: how may I help you?'' speech categorization
task.
Joint work with Rob Schapire (AT&T Labs).
March 19, 5 p.m., Room 158, Department of Psychology,
University of Denver (2155 S. Race St.): Marie Banich, U. Illinois
The role of the corpus collosum in modulating attention
CANCELLED
March 30: Mike Mozer / Randy O'Reilly
Practice talks for Cognitive Neuroscience Conference:
Temporal dynamics of cognition (Mozer)
Principles for Learning and Processing in the Cortex: Reconciling
Interactivity and Generalization using Competition and Hebbian Learning
(O'Reilly)
April 2, 12:00-1:00 p.m., Muenzinger D430: Ray Jackendoff, MIT
What's in the Lexicon?
A basic psychological question that has not received quite enough attention
in the theoretical literature is: What words and uses of words must be
stored in long-term memory, and which can be computed online in short-term
memory? This question leads to a distinction between two kinds of
regularities that have both been called 'lexical rules': productive rules
for which one always knows, given an appropriate input, what the output
will be; and semiproductive rules, for which one has to know whether the
output is an actual word and what its peculiarities might be. I will argue
that although the outputs of semiproductive processes must be stored, those
of productive processes may be composed online (and in many cases must be).
This of course has an important bearing on the hot issue of 'one or two
systems' in morphology.
April 6: Michael Littman
This is the regular BCLG meeting, but will be held in Muenzinger D430
April 17, 10-12, ECOT 831a, Peter Marbach
Reinforcement learning theory
April 20: Manav Misra
Modeling task-dependent performance in visual search with reinforcement
learning
April 27: John Williams
Experiments with Memoryless Algorithm to Learn Optimal Stochastic
Policies for Partially Observable Markov Decision Processes
Partially Observable Markov Decision Problems (POMDPs) are an important
class of learning problems which present unique theoretical and computational
difficulties. In the absence of the Markov property, classical reinforcement
learning techniques may no longer be effective, and memory-based approaches
to state-estimation are notoriously computationally intensive. An alternative
solution is to learn an optimal stochastic policy, which for each observation
of the environment prescribes a probability distribution over available
actions that maximizes the agent's average reward per timestep. Such a
policy achieves the best performance possible when the agent acts based on
immediate observations alone.
A reinforcement learning algorithm which learns locally optimal stochastic
policies was proposed by Jaakkola, Singh and Jordan, but not experimentally
verified. In this talk I will present this algorithm, discuss its
implementation, and demonstrate its effectiveness in solving several POMDPs
including a maze, a matrix game, and a dynamic routing problem.