Connectionists: NIPS 2006 workshop: Revealing Hidden Elements of Dynamical Systems -- Announcement and Call for Papers

[Elad Yom-Tov]

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                        Call For Papers

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

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

              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: 

?        What are the special features of dynamical system learning that 
separate it from other learning problems? 
?        What are the pros and cons of the current methods? 
?        How can statistical and information theoretic techniques be 
combined with the theoretical structure of dynamical systems? 
?        What kind of optimization principles for learning dynamics can be 
derived? 
?        Are there generic features that can be extracted from time-series 
data? 
?        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 (yomtov at il.ibm.com) no later than 31st October, specifying: 
?        Title 
?        Authors and affiliations 
?        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
==================

October  31: Abstract Submission
November 13: Acceptance Notification


Organizing Committee
=====================

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