Two technical workshops will be held at ICML 2001:
Machine Learning for Spatial and Temporal Data
Hierarchy and Memory in Reinforcement Learning
The submission deadlines for both ICML 2001 workshops is April 2. The
workshops are briefly described below. Full details and submission
instructions can be found at the workshop websites.
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MACHINE LEARNING FOR SPATIAL AND TEMPORAL DATA
http://www.cs.orst.edu/~tgd/workshop.html
What is the next generic tool for machine learning? This workshop will
explore how it might be possible to construct a general purpose tool for
learning from temporal and spatial data. Many emerging applications of
machine learning require learning a mapping y = F(x) where the x's and
the y's are complex objects such as time series, sequences,
2-dimensional
maps, images, GIS layers, etc. Examples of such applications include
various forms of information extraction, landcover prediction in remote
sensing, protein secondary structure prediction, identifying fraudulent
transactions, computer intrusion detection, and classical problems such
as text-to-speech mapping and speech recognition. The purpose of this
workshop is to bring together researchers from several fields to discuss
research and application challenges in this area. Specifically, we will
ask the participants to identify the various existing approaches to
learning from spatial and temporal data, the state of the underlying
theory, the state of existing tools and tool kits, and the prospects for
developing new off-the-shelf tools.
Thomas G. Dietterich, Oregon State University
http://www.cs.orst.edu/~tgd
tgd@cs.orst.edu
Foster Provost, NYU
http://www.stern.nyu.edu/~fprovost/
fprovost@stern.nyu.edu
Padhraic Smyth, UC Irvine
http://www.ics.uci.edu/~smyth/
smyth@ics.uci.edu
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HIERARCHY AND MEMORY IN REINFORCEMENT LEARNING
http://www-anw.cs.umass.edu/~ajonsson/icml/
In recent years, much of the research in reinforcement learning has
focused on learning, planning, and representing knowledge at multiple
levels of temporal abstraction. If reinforcement learning is to scale to
solving larger, more real-world-like problems, it is essential to
consider hierarchical approaches in which complex learning tasks are
decomposed into subtasks. It has been shown in recent and past work
that
a hierarchical approach substantially increases the efficiency and
abilities of RL systems. Some recent work has shown additional benefits
from using memory in reinforcement learning, both alone and in
combination with hierarchical approaches. This workshop will be an
opportunity for the researchers in this growing field to share knowledge
and expertise on the topic, open lines of communication for
collaboration, prevent redundant research, and possibly agree on
standard
problems and techniques.
David Andre, UC Berkeley
dandre@cs.berkeley.edu
http://www.cs.berkeley.edu/~dandre/
Anders Jonsson, Univ. of Massachusetts, Amherst
ajonsson@cs.umass.edu
http://www-anw.cs.umass.edu/~ajonsson/
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