Explainability

Explaining Deep Tractable Probabilistic Models: The sum-product network case PGM 2022 Explaining Preferences with Shapley Values NeurIPS 2022 FastSHAP: Real-Time Shapley Value Estimation ICLR 2022 Fooling Explanations in Text Classifiers ICLR 2022 Learning with Domain Knowledge to Develop Justifiable Convolutional Networks ACML 2022 Relevance for Robust Bayesian Network MAP-Explanations PGM 2022 Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions ICLR 2021 Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings EMNLP 2021 Evaluation of Similarity-based Explanations ICLR 2021 Generating Deep Networks Explanations with Robust Attribution Alignment ACML 2021 Getting a CLUE: A Method for Explaining Uncertainty Estimates ICLR 2021 Learning "What-if" Explanations for Sequential Decision-Making ICLR 2021 RADAR-X: An Interactive Interface Pairing Contrastive Explanations with Revised Plan Suggestions AAAI 2021 Explanation by Progressive Exaggeration ICLR 2020 Learn to Explain Efficiently via Neural Logic Inductive Learning ICLR 2020 Formal Verification of Bayesian Network Classifiers PGM 2018 Same-Decision Probability: Threshold Robustness and Application to Explanation PGM 2018