CoShap: A Scalable Coalition Growth Approach to Shapley Value Approximation
Summary: CoShap approximates Shapley values via layer-wise coalition growth, reusing utilities to avoid Monte Carlo redundancy, followed by variance-aware feature evaluation. Dynamic cross-phase budgeting provides (ε,δ) guarantees, delivering up to 7.15× speedup and 76.84% lower error. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Jingxuan He (Guangxi University; Harbin Engineering University)
- 2. Changshuo Liu (National University of Singapore)
- 3. Shaofeng Cai (National University of Singapore)
- 4. Xixian Han (Harbin Engineering University)
- 5. Yanyan Shen (Shanghai Jiao Tong University)
- 6. Beng Chin Ooi (Zhejiang University)
BibTeX Citation
@inproceedings{he_sigmod26,
title = {{CoShap: A Scalable Coalition Growth Approach to Shapley Value Approximation}},
author = {He, Jingxuan and Liu, Changshuo and Cai, Shaofeng and Han, Xixian and Shen, Yanyan and Ooi, Beng Chin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802032},
url = {https://dl.acm.org/doi/10.1145/3802032},
year = {2026}
}
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