Interpretability

Tree in Tree: from Decision Trees to Decision Graphs NeurIPS 2021 Uncovering Implicit Gender Bias in Narratives through Commonsense Inference EMNLP 2021 User Driven Model Adjustment via Boolean Rule Explanations AAAI 2021 Verification and Repair of Neural Networks AAAI 2021 What Makes a Scientific Paper be Accepted for Publication? EMNLP 2021 A Diagnostic Study of Explainability Techniques for Text Classification EMNLP 2020 A Disentangling Invertible Interpretation Network for Explaining Latent Representations CVPR 2020 AI Trust in Business Processes: The Need for Process-Aware Explanations AAAI 2020 A Model-Driven Deep Neural Network for Single Image Rain Removal CVPR 2020 Are “Undocumented Workers” the Same as “Illegal Aliens”? Disentangling Denotation and Connotation in Vector Spaces EMNLP 2020 A Tale of a Probe and a Parser ACL 2020 Attack to Explain Deep Representation CVPR 2020 Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses NeurIPS 2020 Decisions, Counterfactual Explanations and Strategic Behavior NeurIPS 2020 Detecting Interactions from Neural Networks via Topological Analysis NeurIPS 2020 Discovering Symbolic Models from Deep Learning with Inductive Biases NeurIPS 2020 DNNs as Layers of Cooperating Classifiers AAAI 2020 Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability AAAI 2020 Efficient Estimation of Influence of a Training Instance EMNLP 2020 Evaluating Attribution for Graph Neural Networks NeurIPS 2020 Evaluating Attribution Methods using White-Box LSTMs EMNLP 2020 Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL 2020 Explainable Data Decompositions AAAI 2020 Explaining Knowledge Distillation by Quantifying the Knowledge CVPR 2020 Exploring the Linear Subspace Hypothesis in Gender Bias Mitigation EMNLP 2020