Perturbation Sensitivity of Maximum-Likelihood Pairwise Ranking in Computational Decision Systems
Maximum-likelihood pairwise ranking is a com- mon computational mechanism for prioritization, reputation estimation, and comparison-driven decision support. Despite its broad use, the perturbation sensitivity of this estimator under structured changes in comparison data remains insufficiently characterized. We study this question as an applied-mathematics and computational-science problem in stability analysis. We for- mulate coordinated perturbation as a budgeted subset-selection problem over pairwise observations and introduce an Adaptive Subset Selection Attack (ASSA) as a scalable search heuristic for probing high-impact perturbation sets. Through experiments on synthetic and observed...
| Authors | Junyi Yao, Zihao Zheng, Jiayu Long |
|---|---|
| Primary category | cs.LG |
| Published | 20 April 2026 |
| Last updated at source | 7 October 2026 |
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