Call for Papers: AIGM’12: algorithmic issues for inference in graphical models

Web site:
In the the framework of the ECAI’2012 conference
August 27/28 2012

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 mathematical modelling is
an adequate framework to understand, 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 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.


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. We expect both

(i) reviews that analyze similarities and differences between
approaches developed by computer scientists and statisticians
in these areas, and

(ii) original papers that propose new algorithms and show their
performance on data sets as compared to state-of-the-art

Important dates

* Submission deadline : 31st of May
* Notification to authors: 29th of June
* Submission of final version: 13th of July

In the name of the organisation committee
N. Peyrard, S. Robin, T. Schiex