mlsanityData to 14 Oct 2026

Explaining Preferences with Shapley Values

arXiv 2205.13662 ↗First posted 26 May 2022stat.ML cs.LG stat.MEcited by 6

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Abstract (arXiv)

While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propose textscPref-SHAP, a Shapley value-based model explanation framework for pairwise comparison data. We derive the appropriate value functions for preference models and further extend the framework to model and explain context specific information, such as the surface type in a tennis game. To demonstrate the utility of textscPref-SHAP, we apply our method to a variety of synthetic and real-world datasets and show that richer and more insightful explanations can be obtained over the baseline.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 26 May 2022.

Builds on

19 of 50 references.

References · 19

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 364
  4. 2018Consistent Individualized Feature Attribution for Tree Ensemblescited by 285
  5. 2020Explaining by Removing: A Unified Framework for Model Explanationcited by 218
  6. 2019Feature relevance quantification in explainable AI: A causal problemcited by 167
  7. 2020True to the Model or True to the Data?cited by 126
  8. 2019Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainabilitycited by 114
  9. 2020Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Modelscited by 107
  10. 2020Kernel methods through the roof: handling billions of points efficientlycited by 89
  11. 2017Preferential Bayesian Optimizationcited by 74
  12. 2021GraphSVX: Shapley Value Explanations for Graph Neural Networkscited by 64
7 more
  1. 2021RKHS-SHAP: Shapley Values for Kernel Methodscited by 30
  2. 2021On Locality of Local Explanation Modelscited by 25
  3. 2020Learning Inconsistent Preferences with Gaussian Processescited by 12
  4. 2022Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regressioncited by 11
  5. 2022Lead-lag detection and network clustering for multivariate time series with an application to the US equity marketcited by 11
  6. 2020Spectral Ranking with Covariatescited by 8
  7. 2022Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanationscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 2.

6 papers.

  1. 2023Looping in the Human Collaborative and Explainable Bayesian Optimizationcited by 27
  2. 2023Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Modelscited by 19
  3. 2024RankSHAP: Shapley Value Based Feature Attributions for Learning to Rankcited by 4
  4. 2025FLoRA: Sample-Efficient Preference-based RL via Low-Rank Style Adaptation of Reward Functionscited by 3
  5. 2026Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approachcited by 0
  6. 2026Aggregate-then-Calibrate for Human-centered Assessment with Theoretical Guaranteescited by 0