mlsanityData to 7 Oct 2026

Priority-Aware Shapley Value

arXiv 2602.09326 ↗First posted 10 Feb 2026cs.LGcited by 2

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Abstract (arXiv)

Shapley values are widely used for model-agnostic data valuation and feature attribution, yet they implicitly assume contributors are interchangeable. This can be problematic when contributors are dependent (e.g., reused/augmented data or causal feature orderings) or when contributions should be adjusted by factors such as trust or risk. We propose Priority-Aware Shapley Value (PASV), which incorporates both hard precedence constraints and soft, contributor-specific priority weights. PASV is applicable to general precedence structures, recovers precedence-only and weight-only Shapley variants as special cases, and is uniquely characterized by natural axioms. We develop an efficient adjacent-swap Metropolis-Hastings sampler for scalable Monte Carlo estimation and analyze limiting regimes induced by extreme priority weights. Experiments on data valuation (MNIST/CIFAR10) and feature attribution (Census Income) demonstrate more structure-faithful allocations and a practical sensitivity analysis via our proposed "priority sweeping".

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 40 references that are in the map. First posted on arXiv 10 Feb 2026.

Builds on

24 of 40 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  5. 2022Generative Adversarial Networkscited by 5,603
  6. 2022Flow Matching for Generative Modelingcited by 5,426
  7. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
  8. 2019The many Shapley values for model explanationcited by 341
  9. 2019Explaining individual predictions when features are dependent: More accurate approximations to Shapley valuescited by 205
  10. 2023Data-centric Artificial Intelligence: A Surveycited by 202
  11. 2019Feature relevance quantification in explainable AI: A causal problemcited by 164
  12. 2021Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learningcited by 162
12 more
  1. 2020Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regressioncited by 153
  2. 2022Data Banzhaf: A Robust Data Valuation Framework for Machine Learningcited by 126
  3. 2019Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilitycited by 114
  4. 2020Shapley explainability on the data manifoldcited by 88
  5. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  6. 2025Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Valuescited by 19
  7. 2022Variance reduced Shapley value estimation for trustworthy data valuationcited by 16
  8. 2024One Sample Fits All: Approximating All Probabilistic Values Simultaneously and Efficientlycited by 9
  9. 2026PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regressioncited by 6
  10. 2020Replication-Robust Payoff-Allocation for Machine Learning Data Marketscited by 3
  11. 2025Faithful Group Shapley Valuecited by 2
  12. 2025Rethinking Data Value: Asymmetric Data Shapley for Structure-Aware Valuation in Data Markets and Machine Learning Pipelinescited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Quotient Semivalues for False-Name-Resistant Data Attributioncited by 1
  2. 2026Generalized Priority-Aware Shapley Valuecited by 0