Shapley Based Residual Decomposition for Instance Analysis
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Abstract (arXiv)
In this paper, we introduce the idea of decomposing the residuals of regression with respect to the data instances instead of features. This allows us to determine the effects of each individual instance on the model and each other, and in doing so makes for a model-agnostic method of identifying instances of interest. In doing so, we can also determine the appropriateness of the model and data in the wider context of a given study. The paper focuses on the possible applications that such a framework brings to the relatively unexplored field of instance analysis in the context of Explainable AI tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 30 May 2023.
Builds on
13 of 42 references.
References · 13
- 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
- 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
- 2017Towards A Rigorous Science of Interpretable Machine Learningcited by 1,400
- 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
- 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 617
- 2019Towards Efficient Data Valuation Based on the Shapley Valuecited by 279
- 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
- 2022If Influence Functions are the Answer, Then What is the Question?cited by 142
- 2020A Distributional Framework for Data Valuationcited by 79
- 2013Bounding the Estimation Error of Sampling-based Shapley Value Approximationcited by 56
- 2021Shapley values for feature selection: The good, the bad, and the axiomscited by 54
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.