[UAI] NIPS workshop: Revealing Hidden Elements of Dynamical Systems 
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Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Call For Papers

Â  Â Revealing Hidden Elements of Dynamical Systems
Â  Â  Â http://www.haifa.il.ibm.com/Workshops/nips2006/index.html

Â  Â  Â  Â  Â  Workshop held at the 20th Annual Conference
Â  Â  Â  Â  Â  Â  on Neural Information Processing Systems
Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  Â  (NIPS 2006)

Â  Â  Â  Â  Â  Â  Â  Whistler, CANADA: December 8 or 9, 2006
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Revealing and modeling the hidden state-space of dynamical systems is a
fundamental problem in signal processing, control theory, and learning.
Classical approaches to this problem include Hidden Markov Models,
Reinforcement Learning, and various system identification algorithms. More
recently, the problem has been approached by such modern machine learning
techniques as kernel methods, Bayesian and Gaussian processes, latent
variables, and the Information Bottleneck. Moreover, dynamic state-space
learning is the key mechanism in the way organisms cope with complex
stochastic environments such as biological adaptation. One familiar example
of a complex dynamic system is the authorship system in the NIPS community.
Such a system can be described by both internal variables, such as links
between NIPS authors, and external environment variables, such as other
research communities. This complex system, which generates a vast number of
papers each year, can be modeled and investigated using various parametric
and non-parametric methods.

In this workshop, we intend to review and confront different approaches to
dynamical system learning, with various applications in machine learning
and neuroscience. We plan to discuss relations between the different
approaches, and address a range of questions and applications:

â¢ Â  Â  Â  Â What are the special features of dynamical system learning that
separate it from other learning problems?
â¢ Â  Â  Â  Â What are the pros and cons of the current methods?
â¢ Â  Â  Â  Â How can statistical and information theoretic techniques be
combined with the theoretical structure of dynamical systems?
â¢ Â  Â  Â  Â What kind of optimization principles for learning dynamics can be
derived?
â¢ Â  Â  Â  Â Are there generic features that can be extracted from time-series
data?
â¢ Â  Â  Â  Â How can we combine static and time series data for modeling
dynamic systems?


In addition, we hope this workshop will familiarize the machine learning
community with many real-world examples and applications of dynamical
system learning. Such examples will also serve as the basis for the
discussion of such systems in the workshop. A successful outcome of the
workshop would be novel methods for learning and modeling from such data,
as well as providing new conceptual frameworks for the general problem of
adaptation to complex environments.


Format
=======
This will be a one-day workshop. We plan to have around 50% of the workshop
devoted to diverse short talks. The rest of the time would be dedicated to
a panel presentation and discussions.

Submission Instructions
========================
If you would like to present at this workshop, please send an email to Elad
Yom-Tov (ooamxu@il.ibm.com) no later than 31st October, specifying:
â¢ Â  Â  Â  Â Title
â¢ Â  Â  Â  Â Authors and affiliations
â¢ Â  Â  Â  Â A short paper - Length should be no more than 2000 words
(Postscript or PDF format)

If there is interest among workshop participants, we may publish an edited
volume of the proceedings after the workshop.


Dates & Deadlines
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October Â 31: Abstract Submission
November 13: Acceptance Notification


Organizing Committee
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Naftali Tishby
Hebrew University, Israel

Michal Rosen-Zvi
IBM Haifa Research Lab, Israel

Elad Yom-Tov
IBM Haifa Research Lab, Israel

Pierre Baldi
University of California at Irvine, USA


Invited Speakers
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Ziv Bar-Joseph
Carnegie Mellon University, USA

Jim Crutchfield
University of California at Davis, USA

Irina Rish
IBM T.J. Watson Research Center, USA


