NeurIPS 2015spotlight21 citations
Submodular Hamming Metrics
Jennifer A Gillenwater, Rishabh K Iyer, Bethany Lusch, Rahul Kidambi, Jeff A. Bilmes
Abstract
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).
BibTeX
@inproceedings{NIPS2015_ba1b3eba,
author = {Gillenwater, Jennifer A and Iyer, Rishabh K and Lusch, Bethany and Kidambi, Rahul and Bilmes, Jeff A},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Submodular Hamming Metrics},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/ba1b3eba322eab5d895aa3023fe78b9c-Paper.pdf},
volume = {28},
year = {2015}
}