[robotics-worldwide] CFP: ICML 2013 Workshop on Spectral Learning

Borja Balle Pigem bballe at lsi.upc.edu
Mon Mar 4 11:52:13 PST 2013

*Call for Papers*: *Workshop on Spectral Learning* --
June 20 or 21, Atlanta (GA), USA
Website: http://sites.google.com/site/spectrallearningworkshop/

Many problems in machine learning involve collecting high-dimensional
multivariate observations or sequences of observations, and then fitting a
compact model which explains these observations. Recently, linear algebra
techniques have given a fundamentally different perspective on how to fit
and perform inference in these models. Exploiting the underlying spectral
properties of the model parameters has led to fast, provably consistent
methods for parameter learning that stand in contrast to previous
approaches, such as Expectation Maximization, which suffer from slow
convergence and issues related to local optima.

In the past several years, these spectral learning algorithms have become
increasingly popular. They have been applied to learn the structure and
parameters of many models including predictive state representations,
finite state transducers, hidden Markov models, latent trees, latent
junction trees, probabilistic context free grammars, and mixture/admixture
models. Spectral learning algorithms have also been applied to a wide range
of application domains including system identification, video modeling,
speech modeling, robotics, and natural language processing.

The focus of this workshop will be on spectral learning algorithms, broadly
construed as any method that fits a model by way of a spectral
decomposition of moments of (features of) observations. We would like the
workshop to be as inclusive as possible and encourage paper submissions and
participation from a wide range of research related to this focus. This
includes (but is not limited to):

   - Linear-algebraic methods for *estimation and inference in
   probabilistic models* and weighted automata/operator models
   - Spectral approaches to *dimension reduction* (e.g., with applications
   in estimating mixture models)
   - Method-of-moment estimation via *higher-order tensor decompositions*
   - Spectral graph theory and applications in *clustering and learning on
   - *Domain-specific aspects* of using spectral approaches in applications

Submitted papers should be in the ICML 2013
format<http://icml.cc/2013/?page_id=25> with
a maximum of *4 pages* (not including references). Please e-mail your
submission to spectralicml2013 at gmail.com with the subject line "*Submission
to Spectral Learning Workshop*". Contributions will be considered for both
short talks and poster presentations.

Concurrent submissions to the workshop and the main conference (or other
conferences) are permitted.

*Important dates:*

   - Submission deadline: *April 6, 2013*
   - Notification of acceptance: *April 20, 2013 (tentative)*
   - Workshop: *June 20 or 21, 2013*


   - Byron Boots (University of Washington)
   - Daniel Hsu (Microsoft Research New England)
   - Borja Balle (Universitat Politècnica de Catalunya)
   - Ankur Parikh (Carnegie Mellon University)

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