The paper provides a unifying perspective of tree-decomposition algorithms appearing in various automated reasoning areas such as join-tree clustering for constraint-satisfaction ...
Kalev Kask, Rina Dechter, Javier Larrosa, Avi Dech...
Matrix factorization algorithms are frequently used in the machine learning community to find low dimensional representations of data. We introduce a novel generative Bayesian pro...
Abstract. We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use lan...
The paper introduces a generalization for known probabilistic models such as log-linear and graphical models, called here multiplicative models. These models, that express probabi...
Localization is a fundamental challenge for autonomous robotics. Although accurate and efficient techniques now exist for solving this problem, they require explicit probabilistic...
Armita Kaboli, Michael H. Bowling, Petr Musí...