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Found 5 papers, 1 papers with code
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
In our work, we focus on a wide class of linear game values, as well as coalitional values, for the marginal game based on a given ML model and predictor vector.
On marginal feature attributions of tree-based models
We exploit the symmetry to derive an explicit formula, with improved complexity and only in terms of the internal model parameters, for marginal Shapley (and Banzhaf and Owen) values of CatBoost models.
Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics
The post-processing methodology involves reshaping the predictor distributions by balancing the positive and negative bias explanations and allows for the regressor bias to decrease.
Stability theory of game-theoretic group feature explanations for machine learning models
The first part of our work formulates a stability theory for these explanation operators by establishing certain bounds for both marginal and conditional explanations.
Computer Science and Game Theory Probability 91A06, 91A12, 91A80, 46N30, 46N99, 68T01
Wasserstein-based fairness interpretability framework for machine learning models
The objective of this article is to introduce a fairness interpretability framework for measuring and explaining the bias in classification and regression models at the level of a distribution.
