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)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
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}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,805 | WeShap: Weak Supervision Source Evaluation with Shapley Values | 2025 | VLDB |
| 2 | 13,292 | Scalable GNN Explanations with Distributed Shapley Values | 2026 | VLDB |
| 3 | 13,324 | ShaRP: Explaining Rankings and Preferences with Shapley Values | 2025 | VLDB |
| 4 | 6,783 | Counterfactual Explanation of Shapley Value in Data Coalitions | 2024 | VLDB |
| 5 | 1,066 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB |
| 6 | 8,250 | P-Shapley: Shapley Values on Probabilistic Classifiers | 2024 | VLDB |
| 7 | 10,796 | Understanding the Black Box: A Deep Empirical Dive into Shapley Value Approximations for Tabular Data | 2025 | SIGMOD |
| 8 | 10,906 | A Comprehensive Study of Shapley Value in Data Analytics | 2025 | VLDB |
| 9 | 6,551 | Efficient Sampling Approaches to Shapley Value Approximation | 2023 | SIGMOD |
| 10 | 13,315 | ShapX Engine: A Demonstration of Shapley Value Approximations | 2025 | SIGMOD |