A consensus-driven probabilistic approach generates robust counterfactual explanations by modeling data under varying classifier agreement levels using conditional normalizing flows.
In: Pro- ceedings of the 32nd International Conference on Machine Learning - Volume 37
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A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations
A consensus-driven probabilistic approach generates robust counterfactual explanations by modeling data under varying classifier agreement levels using conditional normalizing flows.