variable importance

How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data AAAI 2025 Leveraging Predictive Equivalence in Decision Trees ICML 2025 Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence ICML 2025 Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping AISTATS 2025 Decorrelated Variable Importance JMLR 2024 Fréchet Random Forests for Metric Space Valued Regression with Non Euclidean Predictors JMLR 2024 MMD-based Variable Importance for Distributional Random Forest AISTATS 2024 Total Variation Floodgate for Variable Importance Inference in Classification ICML 2024 Variable Importance in High-Dimensional Settings Requires Grouping AAAI 2024 Zipper: Addressing Degeneracy in Algorithm-Agnostic Inference NeurIPS 2024 Exploring and Interacting with the Set of Good Sparse Generalized Additive Models NeurIPS 2023 Statistically Valid Variable Importance Assessment through Conditional Permutations NeurIPS 2023 The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance NeurIPS 2023 Bayesian subset selection and variable importance for interpretable prediction and classification JMLR 2022 Exploring the Whole Rashomon Set of Sparse Decision Trees NeurIPS 2022 Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance JMLR 2022 Lazy Estimation of Variable Importance for Large Neural Networks ICML 2022 SHAFF: Fast and consistent SHApley eFfect estimates via random Forests AISTATS 2022 Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees NeurIPS 2022 From global to local MDI variable importances for random forests and when they are Shapley values NeurIPS 2021 Uncertainty-aware sensitivity analysis using Rényi divergences UAI 2021 Efficient nonparametric statistical inference on population feature importance using Shapley values ICML 2020 Model Class Reliance for Random Forests NeurIPS 2020 Significance Tests for Neural Networks JMLR 2020 All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously JMLR 2019