Loop Calculus, introduced by Chertkov and Chernyak, is a new technique to incrementally improve approximations computed by Loopy Belief Propagation (LBP), with the ability to even...
We use a generative history-based model to predict the most likely derivation of a dependency parse. Our probabilistic model is based on Incremental Sigmoid Belief Networks, a rec...
In this paper, we propose a geometric approach to the theory of evidence based on convex geometric interpretations of its two key notions of belief function (b.f.) and Dempster...
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
In [3], we introduced a framework for querying and updating probabilistic information over unordered labeled trees, the probabilistic tree model. The data model is based on trees ...