Dear colleagues,
The fifth edition of the AIGM workshop on Algorithmic Issues for
Inference in Graphical Models will be organized in Paris, on the 28th of
September 2015.
Web page with invited speakers: http://goo.gl/wnfeoJ
Motivation: most real (e.g. biological) complex systems are defined or
modeled 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 modeling is an adequate framework to understand, to be able
to control or to predict the behavior of complex systems, such as gene
regulatory networks, contact networks in epidemiology or complex
molecules. 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 analyze 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.
See the web page for submission details.
Important dates
* Submission deadline : June 12, 2015
* Notification to authors: July 3, 2015
* Submission of final version: July 17, 2015
The organization committee:
S. de Givry, N. Peyrard, S. Robin, R. Sabbadin, T. Schiex
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