Learning Reductions That Really Work.

Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro

Abstract: In this paper, we provide a summary of the mathematical and computational techniques that have enabled learning reductions to effectively address a wide class of tasks, and show that this approach to solving machine learning problems can be broadly useful. Our work is instantiated and tested in a machine learning library, Vowpal Wabbit, to prove that the techniques discussed here are fully viable in practice.