@inproceedings{a3d05defcd3f43c8a768e4ce3e8d75de,
title = "Min-Max Submodular Ranking for Multiple Agents",
abstract = "In the submodular ranking (SR) problem, the input consists of a set of submodular functions defined on a ground set of elements. The goal is to order elements for all the functions to have value above a certain threshold as soon on average as possible, assuming we choose one element per time. The problem is flexible enough to capture various applications in machine learning, including decision trees. This paper considers the min-max version of SR where multiple instances share the ground set. With the view of each instance being associated with an agent, the min-max problem is to order the common elements to minimize the maximum objective of all agents—thus, finding a fair solution for all agents. We give approximation algorithms for this problem and demonstrate their effectiveness in the application of finding a decision tree for multiple agents.",
author = "Qingyun Chen and Sungjin Im and Benjamin Moseley and Chenyang Xu and Ruilong Zhang",
note = "Publisher Copyright: Copyright {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 37th AAAI Conference on Artificial Intelligence, AAAI 2023 ; Conference date: 07-02-2023 Through 14-02-2023",
year = "2023",
month = jun,
day = "27",
doi = "10.1609/aaai.v37i6.25862",
language = "英语",
series = "Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023",
publisher = "AAAI press",
pages = "7061--7068",
editor = "Brian Williams and Yiling Chen and Jennifer Neville",
booktitle = "AAAI-23 Technical Tracks 6",
}