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Found 3 papers, 2 papers with code
Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change
We establish a theoretical framework for probabilistically defining robustness to model change and demonstrate how our BetaRCE method directly stems from it.
A SAT-based approach to rigorous verification of Bayesian networks
Recent advancements in machine learning have accelerated its widespread adoption across various real-world applications.
A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers
Counterfactuals are widely used to explain ML model predictions by providing alternative scenarios for obtaining the more desired predictions.
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