model explanation

A Comparative Study of Faithfulness Metrics for Model Interpretability Methods ACL 2022 Anomaly Explanation IJCAI 2022 AttExplainer: Explain Transformer via Attention by Reinforcement Learning IJCAI 2022 Can Explanations Be Useful for Calibrating Black Box Models? ACL 2022 Consistent Explanations by Contrastive Learning CVPR 2022 Constraint-Driven Explanations for Black-Box ML Models AAAI 2022 DALE: Differential Accumulated Local Effects for efficient and accurate global explanations ACML 2022 Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations AAAI 2022 Explaining Preferences with Shapley Values NeurIPS 2022 ExSum: From Local Explanations to Model Understanding NAACL 2022 FastSHAP: Real-Time Shapley Value Estimation ICLR 2022 FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles AAAI 2022 GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games NeurIPS 2022 How (Un)Faithful is Attention? EMNLP 2022 Human-Centered Evaluation of Explanations NAACL 2022 Human Ratings Do Not Reflect Downstream Utility: A Study of Free-Text Explanations for Model Predictions EMNLP 2022 Improved feature importance computation for tree models based on the Banzhaf value UAI 2022 Learning to Generate Inversion-Resistant Model Explanations NeurIPS 2022 Learning to Scaffold: Optimizing Model Explanations for Teaching NeurIPS 2022 Libra-CAM: An Activation-Based Attribution Based on the Linear Approximation of Deep Neural Nets and Threshold Calibration IJCAI 2022 Manipulating SHAP via Adversarial Data Perturbations (Student Abstract) AAAI 2022 Margin-distancing for safe model explanation AISTATS 2022 Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection NAACL 2022 NoiseGrad — Enhancing Explanations by Introducing Stochasticity to Model Weights AAAI 2022 On the Impact of Temporal Concept Drift on Model Explanations EMNLP 2022