<div class="gmail_quote"><div class="gmail_quote"><div><p>This is the second call for abstracts and participation for the workshop on nonparametric Bayes at ICML/UAI/COLT to be held July 9, 2008. Apologies for cross-posting.</p>
<p>Please note the extended submission deadline of May 9, 2008.</p><p><br></p><p>Nonparametric Bayes 2008<br>
Workshop held at ICML/UAI/COLT 2008<br>
Helsinki, Finland<br>
July 9, 2008<br>
<a href="http://npbayes.wikidot.com/" target="_blank">http://npbayes.wikidot.com</a></p>
<p>One of the major problems driving current research in statistical
machine learning is the search for ways to exploit highly-structured
models that are both expressive and tractable. Nonparametric Bayesian
methodology provides significant leverage on this problem. In the
nonparametric Bayesian framework, the prior distribution is not a fixed
parametric form, but is rather a general stochastic process—-a
distribution over a possibly uncountably infinite number of random
variables. This generality makes it possible to work with prior and
posterior distributions on objects such as trees of unbounded depth and
breadth, graphs, partitions, sets of monotone functions, sets of smooth
functions and sets of general measures.</p>
<p>Applications of nonparametric Bayesian methods have begun to appear
in disciplines such as information retrieval, natural language
processing, machine vision, computational biology, cognitive science
and signal processing. Because of their flexibility, they can also be
used to express prior knowledge without restricting to small parametric
classes. Furthermore, research on nonparametric Bayesian models has
served to enhance the links between statistical machine learning and a
number of other mathematical disciplines, including stochastic
processes, algorithms, optimization, combinatorics and knowledge
representation.</p>
<p>There have been several previous workshops on nonparametric Bayesian
methods at machine learning conferences, including workshops at NIPS in
2003 and 2005 and a workshop at ICML workshop in 2006. This workshop
aims to build on the success of these earlier workshops and to catalyze
further research. There are many problem areas that need additional
attention; these include (1) the development of new Monte Carlo and
variational algorithms for inference; (2) the combination of ideas from
knowledge representation and nonparametric Bayesian analysis to develop
formal languages for specifying and manipulating flexible Bayesian
models; (3) the problem of finding objective priors that work in the
nonparametric Bayesian setting; (4) theoretical analysis of the
conditions under which nonparametric Bayesian methods succeed or fail;
and (5) the ongoing need to find compelling applications that serve to
exhibit recent developments and to drive further research. This
workshop is intended to bring together the growing community of
nonparametric Bayesian researchers to explore these and other issues.</p>
<h3><span>FORMAT:</span></h3>
<p>The one-day workshop consists of three invited talks, three
contributed talks, a round-table discussion on theory, methodology and
applications, a round-table discussion on general-purpose language and
software, a poster session, and a panel discussion.</p>
<h3><span>CALL FOR PARTICIPATION:</span></h3>
<p>Researchers interested in presenting their work and ideas at the workshop should send an email to <span><a href="mailto:npbayes@googlemail.com" target="_blank">npbayes@googlemail.com</a></span> with the following information:</p>
<ul><li>Title</li><li>Authors</li><li>Abstract (maximum 2 pages, ICML style pdf)</li><li>Preferred contribution (talk, poster, and/or round-table participation)</li></ul>
<p>We expect authors to provide a final version of their papers by late
June for inclusion on the workshop home page. Papers chosen for
contributed talks shall also be expected to liaise with a discussion
leader who will be in charge of stimulating discussion of the work at
the workshop.</p>
<h3><span>DATES:</span></h3>
<ul><li>Abstracts due: May 9, 2008</li><li>Notifications: May 16, 2008</li><li>Final paper due: June 20, 2008</li><li>Workshop: July 9, 2008</li></ul>
<h3><span>ORGANIZERS:</span></h3>
<ul><li>Yee Whye Teh. Gatsby Unit, UCL</li><li>Romain Thibaux. Computer Science, Berkeley</li><li>Athanasios Kottas. Applied Mathematics and Statistics, UC Santa Cruz</li><li>Zoubin Ghahramani. Engineering, Cambridge</li>
<li>Michael I. Jordan. Computer Science and Statistics, UC Berkeley</li></ul>
<h3><span>CONTACT:</span></h3>
<p><span><a href="mailto:npbayes@googlemail.com" target="_blank">npbayes@googlemail.com</a></span></p>
</div>
<br></div>
</div><br><br clear="all"><br>-- <br>Yee Whye Teh, Ph.D. +44 20 7679 1199<br>Lecturer, Gatsby Computational Neuroscience Unit, University College London<br><a href="mailto:ywteh@gatsby.ucl.ac.uk">ywteh@gatsby.ucl.ac.uk</a> <a href="http://www.gatsby.ucl.ac.uk/~ywteh">http://www.gatsby.ucl.ac.uk/~ywteh</a>