In this paper we propose an approximated structured prediction framework for large scale graphical models and derive message-passing algorithms for learning their parameters effic...
A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alm...
Compilation is an important approach to a range of inference problems, since it enables linear-time inference in the size S of the compiled representation. However, the main drawb...
In this paper we show that incrementally stable nonlinear time–delay systems admit symbolic models which are approximately equivalent, in the sense of approximate bisimulation, ...
Giordano Pola, Pierdomenico Pepe, Maria Domenica D...
Abstract-- This paper introduces a deterministic approximation algorithm with error guarantees for computing the probability of propositional formulas over discrete random variable...