IJCAI’15 Workshop on Sensitivity Analysis and Robustness in Probabilistic Graphical Models
Buenos Aires, July 25-27, 2015 – http://ipg.idsia.ch/wijcai15/ THIRD CALL FOR PAPERS
Papers will be published in a volume of the JMLR Workshop and Conference Proceedings series.
(http://jmlr.csail.mit.edu/
Probabilistic graphical models are important tools in machine learning and artificial intelligence for reasoning with uncertainty. They provide means to represent large multivariate domains compactly and to perform sophisticated learning and reasoning efficiently. Examples of probabilistic graphical models are Bayesian networks, Markov Random fields, chain and factor graphs, Gaussian graphical models, to name but a few. The quantification of these models usually requires sharp (i.e., precise) assessments of the model local potentials and might be subject to robustness issues. For instance, perturbations of some parameter values may lead to different decisions from those which would be achieved by the unperturbed model, suggesting that decisions are not reliable. Reliability might also be in question because of missing data and assumptions behind the process.
More details about the submission procedure are available online.