model explanation

Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods AISTATS 2022 Prototype-Based Explanations for Graph Neural Networks (Student Abstract) AAAI 2022 Reinforcement Learning Explainability via Model Transforms (Student Abstract) AAAI 2022 Sanity Simulations for Saliency Methods ICML 2022 SHAP-Based Explanation Methods: A Review for NLP Interpretability COLING 2022 Supervising Model Attention with Human Explanations for Robust Natural Language Inference AAAI 2022 Task-Agnostic Graph Explanations NeurIPS 2022 Trading Complexity for Sparsity in Random Forest Explanations AAAI 2022 UNIREX: A Unified Learning Framework for Language Model Rationale Extraction ACL 2022 Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation AAAI 2022 Use-Case-Grounded Simulations for Explanation Evaluation NeurIPS 2022 Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features JMLR 2022 Washing The Unwashable : On The (Im)possibility of Fairwashing Detection NeurIPS 2022 What Does My GNN Really Capture? On Exploring Internal GNN Representations IJCAI 2022 Alibi Explain: Algorithms for Explaining Machine Learning Models JMLR 2021 An Analysis of LIME for Text Data AISTATS 2021 A Unified Taylor Framework for Revisiting Attribution Methods AAAI 2021 BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers EACL 2021 Characterizing the risk of fairwashing NeurIPS 2021 Decision-Focused Summarization EMNLP 2021 Evaluation of Similarity-based Explanations ICLR 2021 Evaluations and Methods for Explanation through Robustness Analysis ICLR 2021 Explaining by Removing: A Unified Framework for Model Explanation JMLR 2021 Explaining Explanations: Axiomatic Feature Interactions for Deep Networks JMLR 2021 Explaining Time Series Predictions with Dynamic Masks ICML 2021