Structured Learning Modulo Theories
This addresses the challenge of modeling and learning in hybrid domains, which is crucial for tasks like automatic de novo design, though it appears incremental as it builds on existing methods.
The paper tackles the problem of learning in hybrid domains with both Boolean and numerical variables by introducing Structured Learning Modulo Theories, a max-margin approach based on Satisfiability Modulo Theories, and validates it on artificial and real-world scenarios.
Modelling problems containing a mixture of Boolean and numerical variables is a long-standing interest of Artificial Intelligence. However, performing inference and learning in hybrid domains is a particularly daunting task. The ability to model this kind of domains is crucial in "learning to design" tasks, that is, learning applications where the goal is to learn from examples how to perform automatic {\em de novo} design of novel objects. In this paper we present Structured Learning Modulo Theories, a max-margin approach for learning in hybrid domains based on Satisfiability Modulo Theories, which allows to combine Boolean reasoning and optimization over continuous linear arithmetical constraints. The main idea is to leverage a state-of-the-art generalized Satisfiability Modulo Theory solver for implementing the inference and separation oracles of Structured Output SVMs. We validate our method on artificial and real world scenarios.