Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation
Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these values are stable once the training data and model are fixed. In this work, we uncover a systematic vulnerability: even modest changes to the validation set, such as introducing noises, cause directional shifts in Shapley distributions. As noises are added, Shapley values of training samples compress toward zero. We trace this to a noise-induced neighborhood reshuffling effect: perturbations alter the local rank order between validation and training samples, flattening the valuation landscape. Using the KNN-Shaple...
| Authors | Yinan Shen, Ziao Yang, Hongfu Liu |
|---|---|
| Primary category | cs.LG |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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