Abstract
Many optimization problems in machine learning are interpreted as parametric programming problems parametrized by one or more hyper-parameters. Parametric programming techniques have been shown to be useful in many machine learning tasks since it allows us to compute a path of optimal solutions for a range of different hyper-parameter values. In this paper we describe parametric programming techniques for machine learning algorithms, and review several applications and recent research topics in this area.
