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- W4320459066 abstract "Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable and treat categorical attributes as continuous ones, which restricts their real-world applications when categorical attributes have many different values or the model is non-differentiable. To make counterfactual explanation suitable for real-world applications, we propose a novel framework of Model-Agnostic Counterfactual Explanation (MACE), which adopts a newly designed pipeline that can efficiently handle non-differentiable machine-learning models on a large number of feature values. in our MACE approach, we propose a novel RL-based method for finding good counterfactual examples and a gradient-less descent method for improving proximity. Experiments on public datasets validate the effectiveness with better validity, sparsity and proximity." @default.
- W4320459066 created "2023-02-14" @default.
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- W4320459066 date "2022-05-31" @default.
- W4320459066 modified "2023-09-30" @default.
- W4320459066 title "MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation" @default.
- W4320459066 doi "https://doi.org/10.48550/arxiv.2205.15540" @default.
- W4320459066 hasPublicationYear "2022" @default.
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