We propose a novel a framework for deriving approximations for intractable probabilistic models. This framework is based on a free energy (negative log marginal likelihood) and ca...
With the increasing popularity of largescale probabilistic graphical models, even "lightweight" approximate inference methods are becoming infeasible. Fortunately, often...
Abstract. There is currently a large interest in relational probabilistic models. While the concept of context-specific independence (CSI) has been well-studied for models such as ...
The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to ...
Hierarchical probabilistic modeling of discrete data has emerged as a powerful tool for text analysis. Posterior inference in such models is intractable, and practitioners rely on...