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ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
[article]
2022
arXiv
pre-print
Counterfactual examples (CFs) are one of the most popular methods for attaching post-hoc explanations to machine learning (ML) models. However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets. This work aims to overcome these limitations and introduces ReLAX, a model-agnostic algorithm to generate optimal counterfactual explanations.
arXiv:2110.11960v2
fatcat:okjqpstvsvcfdpf7a6qcmd4zpe