Automated CPU Design by Learning from Input-Output Examples
Abstract
We study the complexity of candidate control in participatory budgeting elections. The goal of constructive candidate control is to ensure that a given candidate wins by either adding or deleting candidates from the election (in the destructive setting, the goal is to prevent a given candidate from winning). We show that such control problems are NP-hard to solve for many participatory budgeting voting rules, including Phragmén and Equal-Shares, but there are natural cases with polynomial-time algorithms. We also argue that control by deleting candidates is a useful tool for assessing the performance (or, strength) of initially losing projects, and we support this view with experiments on real-life PB instances.
Cite
Text
Cheng et al. "Automated CPU Design by Learning from Input-Output Examples." International Joint Conference on Artificial Intelligence, 2024. doi:10.24963/ijcai.2024/425Markdown
[Cheng et al. "Automated CPU Design by Learning from Input-Output Examples." International Joint Conference on Artificial Intelligence, 2024.](https://mlanthology.org/ijcai/2024/cheng2024ijcai-automated/) doi:10.24963/ijcai.2024/425BibTeX
@inproceedings{cheng2024ijcai-automated,
title = {{Automated CPU Design by Learning from Input-Output Examples}},
author = {Cheng, Shuyao and Jin, Pengwei and Guo, Qi and Du, Zidong and Zhang, Rui and Hu, Xing and Zhao, Yongwei and Hao, Yifan and Guan, Xiangtao and Han, Husheng and Zhao, Zhengyue and Liu, Ximing and Zhang, Xishan and Chu, Yuejie and Mao, Weilong and Chen, Tianshi and Chen, Yunji},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2024},
pages = {3843-3853},
doi = {10.24963/ijcai.2024/425},
url = {https://mlanthology.org/ijcai/2024/cheng2024ijcai-automated/}
}