aiqbench analysis
This research introduces Local Shapley, an algorithmic advance that leverages model-induced locality to drastically reduce the computational cost of data valuation by reusing training operations across overlapping support sets. By formalizing how only specific subsets of training data influence predictions in various architectures like trees and GNNs, it transforms a #P-hard global problem into a structured processing task with provable information-theoretic bounds. The proposed LSMR methods demonstrate significant speedups while maintaining valuation fidelity, offering a practical solution for scaling explainability in large-scale machine learning systems.
Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation
arXiv:2603.03672v2 Announce Type: replace-cross Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact. This reframes Shapley evaluation as a structured data processing problem over overlapping support-induced subset families rather than exhaustive coalition enumeration. We prove that the intrinsic complexity of Local Shapley is governed by the number of distinct influential subsets, establishing an information-theoretic lower bound on retraining operations. Guided by this result, we propose LSMR (Local Shapley via Model Reuse), an optimal subset-centric algorithm that trains each influential subset exactly once via support mapping and pivot scheduling. For larger supports, we develop LSMR-A, a reuse-aware Monte Carlo estimator that remains unbiased with exponential concentration, with runtime determined by the number of distinct sampled subsets rather than total draws. Experiments across multiple model families demonstrate substantial retraining reductions and speedups while preserving high valuation fidelity.
Abstract:The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a given test instance, only a small subset of training points influences the prediction. We formalize this model-induced locality through support sets defined by the model's computational pathway (e.g., neighbors in KNN, leaves in trees, receptive fields in GNNs), showing that Shapley computation can be projected onto these supports without loss when locality is exact. This reframes Shapley evaluation as a structured data processing problem over overlapping support-induced subset families rather than exhaustive coalition enumeration. We prove that the intrinsic complexity of Local Shapley is governed by the number of distinct influential subsets, establishing an information-theoretic lower bound on retraining operations. Guided by this result, we propose LSMR (Local Shapley via Model Reuse), an optimal subset-centric algorithm that trains each influential subset exactly once via support mapping and pivot scheduling. For larger supports, we develop LSMR-A, a reuse-aware Monte Carlo estimator that remains unbiased with exponential concentration, with runtime determined by the number of distinct sampled subsets rather than total draws. Experiments across multiple model families demonstrate substantial retraining reductions and speedups while preserving high valuation fidelity.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Databases (cs.DB); Computer Science and Game Theory (cs.GT)
arXiv:2603.03672 [cs.LG]
(or arXiv:2603.03672v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2603.03672 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 314 words
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