post-hoc explanation

Explanation-based Data Augmentation for Image Classification NeurIPS 2021 Generating Deep Networks Explanations with Robust Attribution Alignment ACML 2021 Reliable Post hoc Explanations: Modeling Uncertainty in Explainability NeurIPS 2021 Towards Benchmarking the Utility of Explanations for Model Debugging NAACL 2021 Contextualizing Hate Speech Classifiers with Post-hoc Explanation ACL 2020 Generative causal explanations of black-box classifiers NeurIPS 2020 Model Interpretability through the lens of Computational Complexity NeurIPS 2020 Regularizing Black-box Models for Improved Interpretability NeurIPS 2020 Robust and Stable Black Box Explanations ICML 2020 Sanity Checks for Saliency Metrics AAAI 2020 FLEX: Faithful Linguistic Explanations for Neural Net Based Model Decisions AAAI 2019 Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement ACL 2018