https://github.com/ai4co/unsupervised-co-ucom2
[ICML'24] Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
https://github.com/ai4co/unsupervised-co-ucom2
coloring combinatorial-optimization facility-location icml maximum-coverage neural-combinatorial-optimization
Last synced: about 2 months ago
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[ICML'24] Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
- Host: GitHub
- URL: https://github.com/ai4co/unsupervised-co-ucom2
- Owner: ai4co
- License: mit
- Created: 2024-01-27T10:57:27.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-12T02:17:56.000Z (over 1 year ago)
- Last Synced: 2025-09-09T01:21:38.390Z (7 months ago)
- Topics: coloring, combinatorial-optimization, facility-location, icml, maximum-coverage, neural-combinatorial-optimization
- Language: Python
- Homepage: https://arxiv.org/abs/2405.08424
- Size: 23.7 MB
- Stars: 14
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization
Source code for the ICML'24 paper **Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More**, where we study and propose UCom2 (Unsupervised Combinatorial Optimization Under Commonly-involved Conditions).
Paper link: https://arxiv.org/abs/2405.08424

The "facility_location_and_max_cover" folder contains the code for the experiments on facility location and maximum coverage.
The "robust_coloring" folder contains the code for the experiments on robust coloring.
Please check the README file in each folder for the guidance for the experiments on each problem.
### Citation
@inproceedings{bu2024ucom2,
title={Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More},
author={Bu, Fanchen and Jo, Hyeonsoo and Lee, Soo Yong and Ahn, Sungsoo and Shin, Kijung},
booktitle={ICML},
year={2024}
}