{"total":87,"rows":[{"id":"2306.05724","title":"Explaining Predictive Uncertainty with Information Theoretic Shapley Values","created":"2023-06-09","cited_by_count":36,"area":null},{"id":"2309.13775","title":"The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance","created":"2023-09-24","cited_by_count":29,"area":null},{"id":"2401.06654","title":"Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks","created":"2024-01-12","cited_by_count":28,"area":null},{"id":"2303.05981","title":"Feature Importance: A Closer Look at Shapley Values and LOCO","created":"2023-03-10","cited_by_count":23,"area":null},{"id":"2306.04292","title":"Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research","created":"2023-06-07","cited_by_count":22,"area":null},{"id":"2206.05487","title":"Scientific Inference With Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena","created":"2022-06-11","cited_by_count":21,"area":null},{"id":"2406.03348","title":"Position: A Call to Action for a Human-Centered AutoML Paradigm","created":"2024-06-05","cited_by_count":14,"area":null},{"id":"2207.05566","title":"BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence","created":"2022-07-12","cited_by_count":13,"area":null},{"id":"2210.04542","title":"DALE: Differential Accumulated Local Effects for efficient and accurate global explanations","created":"2022-10-10","cited_by_count":11,"area":null},{"id":"2404.12862","title":"A Guide to Feature Importance Methods for Scientific Inference","created":"2024-04-19","cited_by_count":11,"area":null},{"id":"2102.13347","title":"MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA","created":"2021-02-26","cited_by_count":10,"area":null},{"id":"2209.01939","title":"Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams","created":"2022-09-05","cited_by_count":9,"area":null},{"id":"2310.07672","title":"Stabilizing Estimates of Shapley Values with Control Variates","created":"2023-10-11","cited_by_count":9,"area":null},{"id":"2210.03047","title":"Conditional Feature Importance for Mixed Data","created":"2022-10-06","cited_by_count":7,"area":null},{"id":"2309.07593","title":"Statistically Valid Variable Importance Assessment through Conditional Permutations","created":"2023-09-14","cited_by_count":7,"area":null},{"id":"2401.15800","title":"Statistical Significance of Feature Importance Rankings","created":"2024-01-28","cited_by_count":7,"area":null},{"id":"2204.09938","title":"Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees","created":"2022-04-21","cited_by_count":6,"area":null},{"id":"2308.03369","title":"Variable importance for causal forests: breaking down the heterogeneity of treatment effects","created":"2023-08-07","cited_by_count":6,"area":null},{"id":"2310.12842","title":"Model-agnostic variable importance for predictive uncertainty: an entropy-based approach","created":"2023-10-19","cited_by_count":6,"area":null},{"id":"2311.07763","title":"The Disagreement Problem in Faithfulness Metrics","created":"2023-11-13","cited_by_count":6,"area":null},{"id":"2405.13474","title":"Why do explanations fail? A typology and discussion on failures in XAI","created":"2024-05-22","cited_by_count":6,"area":null},{"id":"2510.24815","title":"Tree Ensemble Explainability through the Hoeffding Functional Decomposition and TreeHFD Algorithm","created":"2025-10-28","cited_by_count":6,"area":null},{"id":"2112.10592","title":"Exact Shapley Values for Local and Model-True Explanations of Decision Tree Ensembles","created":"2021-12-16","cited_by_count":5,"area":null},{"id":"2201.08837","title":"Marginal Effects for Non-Linear Prediction Functions","created":"2022-01-21","cited_by_count":5,"area":null},{"id":"2310.03466","title":"The Blame Problem in Evaluating Local Explanations, and How to Tackle it","created":"2023-10-05","cited_by_count":5,"area":null},{"id":"2404.18702","title":"Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots","created":"2024-04-29","cited_by_count":5,"area":null},{"id":"2312.10858","title":"Variable Importance in High-Dimensional Settings Requires Grouping","created":"2023-12-18","cited_by_count":4,"area":null},{"id":"2403.10250","title":"Interpretable Machine Learning for Survival Analysis","created":"2024-03-15","cited_by_count":4,"area":null},{"id":"2410.23772","title":"Disentangling Interactions and Dependencies in Feature Attribution","created":"2024-10-31","cited_by_count":4,"area":null},{"id":"2208.01129","title":"Accelerated and interpretable oblique random survival forests","created":"2022-08-01","cited_by_count":3,"area":null},{"id":"2311.11491","title":"On the Relationship Between Interpretability and Explainability in Machine Learning","created":"2023-11-20","cited_by_count":3,"area":null},{"id":"2506.08928","title":"Local MDI+: Local Feature Importances for Tree-Based Models","created":"2025-06-10","cited_by_count":3,"area":null},{"id":"2104.04295","title":"Transforming Feature Space to Interpret Machine Learning Models","created":"2021-04-09","cited_by_count":2,"area":null},{"id":"2111.08478","title":"Spatial machine-learning model diagnostics: a model-agnostic distance-based approach","created":"2021-11-13","cited_by_count":2,"area":null},{"id":"2211.04409","title":"Individualized and Global Feature Attributions for Gradient Boosted Trees in the Presence of $\\ell_2$ Regularization","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2306.10551","title":"Can predictive models be used for causal inference?","created":"2023-06-18","cited_by_count":2,"area":null},{"id":"2307.01777","title":"Shapley Sets: Feature Attribution via Recursive Function Decomposition","created":"2023-07-04","cited_by_count":2,"area":null},{"id":"2310.08495","title":"Characterizing climate pathways using feature importance on echo state networks","created":"2023-10-12","cited_by_count":2,"area":null},{"id":"2310.12115","title":"MMD-based Variable Importance for Distributional Random Forest","created":"2023-10-18","cited_by_count":2,"area":null},{"id":"2401.00800","title":"Factor Importance Ranking and Selection using Total Indices","created":"2024-01-01","cited_by_count":2,"area":null},{"id":"2404.16534","title":"SIDEs: Separating Idealization from Deceptive Explanations in xAI","created":"2024-04-25","cited_by_count":2,"area":null},{"id":"2205.07271","title":"Supervised Learning and Model Analysis with Compositional Data","created":"2022-05-15","cited_by_count":1,"area":null},{"id":"2304.02490","title":"Opening the random forest black box by the analysis of the mutual impact of features","created":"2023-04-05","cited_by_count":1,"area":null},{"id":"2305.07224","title":"Asymmetric feature interaction for interpreting model predictions","created":"2023-05-12","cited_by_count":1,"area":null},{"id":"2308.16113","title":"survex: an R package for explaining machine learning survival models","created":"2023-08-30","cited_by_count":1,"area":null},{"id":"2404.16495","title":"T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients","created":"2024-04-25","cited_by_count":1,"area":null},{"id":"2407.00843","title":"A Unified Approach to Extract Interpretable Rules from Tree Ensembles via Integer Programming","created":"2024-06-30","cited_by_count":1,"area":null},{"id":"2407.09964","title":"TrIM: Transformed Iterative Mondrian Forests for Gradient-based Dimension Reduction and High-Dimensional Regression","created":"2024-07-13","cited_by_count":1,"area":null},{"id":"2411.18575","title":"Functional relevance based on the continuous Shapley value","created":"2024-11-27","cited_by_count":1,"area":null},{"id":"2509.09482","title":"Database Views as Explanations for Relational Deep Learning","created":"2025-09-11","cited_by_count":1,"area":null},{"id":"2509.15420","title":"Top-$k$ Feature Importance Ranking","created":"2025-09-18","cited_by_count":1,"area":null},{"id":"2603.15306","title":"xplainfi: Feature Importance and Statistical Inference for Machine Learning in R","created":"2026-03-16","cited_by_count":1,"area":null},{"id":"2606.03549","title":"How Many Trees in a Random Forest? A Revisited Approach with Plateau Search and Optuna Integration","created":"2026-06-02","cited_by_count":1,"area":null},{"id":"2206.01284","title":"Sequential Permutation Testing of Random Forest Variable Importance Measures","created":"2022-06-02","cited_by_count":0,"area":null},{"id":"2207.14160","title":"Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark","created":"2022-06-08","cited_by_count":0,"area":null},{"id":"2209.10578","title":"Algorithm-Agnostic Interpretations for Clustering","created":"2022-09-21","cited_by_count":0,"area":null},{"id":"2210.12456","title":"Abstract Interpretation-Based Feature Importance for SVMs","created":"2022-10-22","cited_by_count":0,"area":null},{"id":"2310.15610","title":"Using Slisemap to interpret physical data","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2312.10573","title":"Random Forest Variable Importance-based Selection Algorithm in Class Imbalance Problem","created":"2023-12-17","cited_by_count":0,"area":null},{"id":"2402.03447","title":"Challenges in Variable Importance Ranking Under Correlation","created":"2024-02-05","cited_by_count":0,"area":null},{"id":"2404.12544","title":"Beyond development: Challenges in deploying machine learning models for structural engineering applications","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2405.13846","title":"Regression Trees Know Calculus","created":"2024-05-22","cited_by_count":0,"area":null},{"id":"2406.01982","title":"Variable importance measure for spatial machine learning models with application to air pollution exposure prediction","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2409.08925","title":"Multi forests: Variable importance for multi-class outcomes","created":"2024-09-13","cited_by_count":0,"area":null},{"id":"2409.13342","title":"Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data","created":"2024-09-20","cited_by_count":0,"area":null},{"id":"2410.12209","title":"Global Censored Quantile Random Forest","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2411.02221","title":"Targeted Learning for Variable Importance","created":"2024-11-04","cited_by_count":0,"area":null},{"id":"2411.08821","title":"Conditional Local Importance by Quantile Expectations","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2501.02111","title":"How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data","created":"2025-01-03","cited_by_count":0,"area":null},{"id":"2502.12209","title":"Suboptimal Shapley Value Explanations","created":"2025-02-17","cited_by_count":0,"area":null},{"id":"2503.20758","title":"MindfulLIME: A Stable Solution for Explanations of Machine Learning Models with Enhanced Localization Precision -- A Medical Image Case Study","created":"2025-03-25","cited_by_count":0,"area":null},{"id":"2506.23396","title":"AICO: Feature Significance Tests for Supervised Learning","created":"2025-06-29","cited_by_count":0,"area":null},{"id":"2507.07276","title":"TRIP: A Nonparametric Test to Diagnose Biased Feature Importance Scores","created":"2025-07-09","cited_by_count":0,"area":null},{"id":"2510.00557","title":"Mathematical Theory of Collinearity Effects on Machine Learning Variable Importance Measures","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2510.03096","title":"Adaptive Node Feature Selection For Graph Neural Networks","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2601.18857","title":"Statistical Inference for Explainable Boosting Machines","created":"2026-01-26","cited_by_count":0,"area":null},{"id":"2602.09116","title":"Importance inversion transfer identifies shared principles for cross-domain learning","created":"2026-02-09","cited_by_count":0,"area":null},{"id":"2602.11159","title":"Explaining AI Without Code: A User Study on Explainable AI","created":"2025-12-28","cited_by_count":0,"area":null},{"id":"2602.16352","title":"Machine Learning in Epidemiology","created":"2026-02-18","cited_by_count":0,"area":null},{"id":"2604.11253","title":"Trustworthy Feature Importance Avoids Unrestricted Permutations","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2605.22529","title":"Stabilising Explainability Fragility in Cybersecurity AI: The Impact and Mitigation of Multicollinearity in Public Benchmark Datasets","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.10877","title":"XtrAIn: Training-Guided Occlusion for Feature Attribution","created":"2026-06-09","cited_by_count":0,"area":null},{"id":"2608.23817","title":"A Formal Methodological Framework for Auditing Robustness and Fidelity in Explainable AI: From Application to Trust Certification","created":"2026-08-24","cited_by_count":0,"area":null},{"id":"2609.09428","title":"XAI-Arena: Can LLMs Assess the Quality of XAI Explanations?","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2609.10313","title":"Semiparametric Inference for Conditional Shapley Feature Importance","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2610.13806","title":"FORTE: A Production Framework for Feature Evaluation and Deprecation in Industrial Recommender Systems","created":"2026-10-09","cited_by_count":0,"area":null},{"id":"2610.16816","title":"Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}