ASSS: Adaptive Stratified Sampling for Shapley-like Values
Summary: Introduces ASSS, adaptive stratification for unbiased Shapley-like value estimation under limited sampling budgets, avoiding static n-stratification’s Ω(n² log n) full-coverage requirement. ASSS-A and ASSS-B trade sample efficiency against memory by stratifying after or before sampling. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Junyuan Pang (Zhejiang University)
- 2. Jiayao Zhang (Zhejiang University)
- 3. Jiazheng Song (Zhejiang University)
- 4. Jinfei Liu (Zhejiang University)
- 5. Kui Ren (Zhejiang University)
BibTeX Citation
@inproceedings{pang_sigmod26,
title = {{ASSS: Adaptive Stratified Sampling for Shapley-like Values}},
author = {Pang, Junyuan and Zhang, Jiayao and Song, Jiazheng and Liu, Jinfei and Ren, Kui},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802016},
url = {https://dl.acm.org/doi/10.1145/3802016},
year = {2026}
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