feature importance

Shapley Flow: A Graph-based Approach to Interpreting Model Predictions AISTATS 2021 Shapley Residuals: Quantifying the limits of the Shapley value for explanations NeurIPS 2021 The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations NeurIPS 2021 Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation NeurIPS 2021 Automated Dependence Plots UAI 2020 Benchmarking Deep Learning Interpretability in Time Series Predictions NeurIPS 2020 Channel Pruning Guided by Classification Loss and Feature Importance AAAI 2020 Deep Direct Likelihood Knockoffs NeurIPS 2020 Evaluating and Aggregating Feature-based Model Explanations IJCAI 2020 Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? ACL 2020 Explaining the Explainer: A First Theoretical Analysis of LIME AISTATS 2020 FIND: Human-in-the-Loop Debugging Deep Text Classifiers EMNLP 2020 Generating Interpretable Poverty Maps using Object Detection in Satellite Images IJCAI 2020 Interpretations are Useful: Penalizing Explanations to Align Neural Networks with Prior Knowledge ICML 2020 Learning outside the Black-Box: The pursuit of interpretable models NeurIPS 2020 Learning to Faithfully Rationalize by Construction ACL 2020 Model Agnostic Multilevel Explanations NeurIPS 2020 Neuron Shapley: Discovering the Responsible Neurons NeurIPS 2020 Problems with Shapley-value-based explanations as feature importance measures ICML 2020 RANCC: Rationalizing Neural Networks via Concept Clustering COLING 2020 Towards Interpretation of Pairwise Learning AAAI 2020 Understanding Global Feature Contributions With Additive Importance Measures NeurIPS 2020 Using Small Business Banking Data for Explainable Credit Risk Scoring AAAI 2020 What went wrong and when? Instance-wise feature importance for time-series black-box models NeurIPS 2020 A Benchmark for Interpretability Methods in Deep Neural Networks NeurIPS 2019