Dear colleagues,
The third edition of the AIGM workshop on Algorithmic Issues for
Inference in Graphical Models will be organised in Paris, the
13rd of September 2013.
Web page:http://carlit.toulouse.inra.fr/wikiz/index.php/AIGM13
Motivation
Most real (e.g. biological) complex systems are formed or
modelled by elementary objects that locally interact with each
other. Local properties can often be measured, assessed or
partially observed. On the other hand, global properties that
stem from these local interactions are difficult to
comprehend. It is now acknowledged that a mathematical modelling
is an adequate framework to understand, to be able to control or
to predict the behaviour of complex systems, such as gene
regulatory networks or contact networks in epidemiology.
More precisely, graphical models (GM), which are formed by
variables bound to their interactors by deterministic or
stochastic relationships, allow researchers to model possibly
high-dimensional heterogeneous data and to capture
uncertainty. Analysis, optimal control, inference or prediction
about complex systems benefit from the formalisation proposed by
GM. To achieve such tasks, a key factor is to be able to answer
general queries: what is the probability to observe such event in
this situation ? Which model best represents my data ? What is
the most acceptable solution to a query of interest that
satisfies a list of given constraints ? Often, an exact
resolution cannot be achieved either because of computational
limits, or because of the intractability of the problem.
Objective
The aim of this workshop is to bridge the gap between Statistics
and Artificial Intelligence communities where approximate
inference methods for GM are developed. We are primarily
interested in algorithmic aspects of probabilistic (e.g. Markov
random fields, Bayesian networks, influence diagrams),
deterministic (e.g. Constraint Satisfaction Problems, SAT,
weighted variants, Generalized Additive Independence models) or
hybrid (e.g. Markov logic networks) models. Call for paper
We expect both
(i) reviews that analyse similarities and differences between
approaches developed by computer scientists and statisticians in
these areas, and
(ii) original research papers which propose new algorithms and
show their performance on data sets as compared to
state-of-the-art methods.
Important dates
Submission deadline : June 10, 2013
Notification to authors: July 1, 2013
Submission of final version: July 12, 2013
The organisation committee:
S. de Givry, N. Peyrard, S. Robin, R. Sabbadin, T. Schiex, M. Vignes
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