Can I read Submodular Hamming Metrics on EtoBox?

Submodular Hamming Metrics by Gillenwater, Jennifer; Iyer, Rishabh; Lusch, Bethany; Kidambi, Rahul; Bilmes, Jeff is a scholarly article available to read on EtoBox.

What is Submodular Hamming Metrics about?

We show that there is a largely unexplored class of functions (positive polymatroids) that can define proper discrete metrics over pairs of binary vectors and that are fairly tractable to optimize over. By exploiting submodularity, we are able to give hardness results and approximation algorithms for optimizing over such metrics. Additionally, we demonstrate empirically the effectiveness of these metrics and associated algorithms on both a metric minimization task (a form of clustering) and also a metric maximization task (generating diverse k-best lists).

Author

Gillenwater, Jennifer; Iyer, Rishabh; Lusch, Bethany; Kidambi, Rahul; Bilmes, Jeff

Published

2015

Language

EN