ICML 2019oral11 citations
Multivariate Submodular Optimization
Richard Santiago, F. Bruce Shepherd
Abstract
Submodular functions have found a wealth of new applications in data science and machine learning models in recent years. This has been coupled with many algorithmic advances in the area of submodular optimization: (SO) $\min/\max f(S): S \in \mathcal{F}$, where $\mathcal{F}$ is a given family of feasible sets over a ground set $V$ and $f:2^V \rightarrow \mathbb{R}$ is submodular. In this work we focus on a more general class of
BibTeX
@InProceedings{pmlr-v97-santiago19a,
title = {Multivariate Submodular Optimization},
author = {Santiago, Richard and Shepherd, F. Bruce},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {5599--5609},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/santiago19a/santiago19a.pdf},
url = {https://proceedings.mlr.press/v97/santiago19a.html},
abstract = {Submodular functions have found a wealth of new applications in data science and machine learning models in recent years. This has been coupled with many algorithmic advances in the area of submodular optimization: (SO) $\min/\max f(S): S \in \mathcal{F}$, where $\mathcal{F}$ is a given family of feasible sets over a ground set $V$ and $f:2^V \rightarrow \mathbb{R}$ is submodular. In this work we focus on a more general class of