… propose Learning Modulo Theories (LMT), a class of novel hybrid statistical relational learning
… and rational variables with a structured output learning method. In particular, we rely on …

… Learning Modulo Theories (LMT), a learning framework capable of dealing with hybrid domains
by combining structured learning with Satisfiability Modulo Theory (SMT) … within learning …

… Training structured-output SVMs requires a separation oracle for generating … structure for
a certain input. Both tasks can be accomplished by a generalized Satisfiability Modulo Theory …

… theory is called Satisfiability Modulo Theories, or … structured as follows. Section 33.2 provides
background information, with formal preliminaries and a brief description of a few theories …

… In the following two sections we give a short overview of SMT and detail how it can be
employed with the structured output SVM framework, then we describe a few applications that can …

… an approach to model learning that is based on using satisfiability modulo theories (SMT) …
is structured as follows. Section 2 describes how one can encode the problem of learning a …

… This paper is structured as follows. Section 2 provides a brief outline of OptiMathSAT
architecture, followed by a description of its optimization functionalities and interfaces in Sect. 3. …

… Learning modulo theories was recently introduced [20] as a framework for adapting
structured-output learning to hybrid domains by leveraging Max-SMT technology. This paper adapts …

… modulo theories (SMT) check the satisfiability of first-order formulas containing operations
from various theories … We briefly survey the theory of SAT and SMT solving, and present some …

… Learning modulo theories was recently introduced [12] as a framework for adapting
structured-output learning to hybrid domains by leveraging Max-SMT technology. This paper adapts …