{"total":126,"rows":[{"id":"2207.07605","title":"Algorithms to estimate Shapley value feature attributions","created":"2022-07-15","cited_by_count":115,"area":null},{"id":"2203.00870","title":"Faith-Shap: The Faithful Shapley Interaction Index","created":"2022-03-02","cited_by_count":69,"area":null},{"id":"2209.04012","title":"From Shapley Values to Generalized Additive Models and back","created":"2022-09-08","cited_by_count":60,"area":null},{"id":"2106.12543","title":"Synthetic Benchmarks for Scientific Research in Explainable Machine Learning","created":"2021-06-23","cited_by_count":59,"area":null},{"id":"2410.01649","title":"shapiq: Shapley Interactions for Machine Learning","created":"2024-10-02","cited_by_count":43,"area":null},{"id":"2105.00108","title":"Explaining a Series of Models by Propagating Shapley Values","created":"2021-04-30","cited_by_count":37,"area":null},{"id":"2306.05724","title":"Explaining Predictive Uncertainty with Information Theoretic Shapley Values","created":"2023-06-09","cited_by_count":36,"area":null},{"id":"2401.12069","title":"Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles","created":"2024-01-22","cited_by_count":31,"area":null},{"id":"2110.09167","title":"RKHS-SHAP: Shapley Values for Kernel Methods","created":"2021-10-18","cited_by_count":30,"area":null},{"id":"2109.09847","title":"Fast TreeSHAP: Accelerating SHAP Value Computation for Trees","created":"2021-09-20","cited_by_count":29,"area":null},{"id":"2106.14648","title":"On Locality of Local Explanation Models","created":"2021-06-24","cited_by_count":25,"area":null},{"id":"2110.14270","title":"Counterfactual Shapley Additive Explanations","created":"2021-10-27","cited_by_count":22,"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":"2106.12228","title":"groupShapley: Efficient prediction explanation with Shapley values for feature groups","created":"2021-06-23","cited_by_count":21,"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":"2111.13507","title":"Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features","created":"2021-11-26","cited_by_count":19,"area":null},{"id":"2202.12270","title":"Evaluating Feature Attribution Methods in the Image Domain","created":"2022-02-22","cited_by_count":19,"area":null},{"id":"2305.15167","title":"Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models","created":"2023-05-24","cited_by_count":19,"area":null},{"id":"2208.06151","title":"Unifying local and global model explanations by functional decomposition of low dimensional structures","created":"2022-08-12","cited_by_count":18,"area":null},{"id":"2104.06164","title":"Fast Hierarchical Games for Image Explanations","created":"2021-04-13","cited_by_count":15,"area":null},{"id":"2201.10528","title":"Explainability in Music Recommender Systems","created":"2022-01-25","cited_by_count":15,"area":null},{"id":"2305.09536","title":"A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them","created":"2023-05-16","cited_by_count":15,"area":null},{"id":"2402.02631","title":"Learning to Understand: Identifying Interactions via the M\\\"obius Transform","created":"2024-02-04","cited_by_count":13,"area":null},{"id":"2208.09970","title":"Statistical Aspects of SHAP: Functional ANOVA for Model Interpretation","created":"2022-08-21","cited_by_count":12,"area":null},{"id":"2102.06416","title":"Explaining predictive models using Shapley values and non-parametric vine copulas","created":"2021-02-12","cited_by_count":9,"area":null},{"id":"2106.03820","title":"Accurate Shapley Values for explaining tree-based models","created":"2021-06-07","cited_by_count":9,"area":null},{"id":"2107.12436","title":"Feature Synergy, Redundancy, and Independence in Global Model Explanations using SHAP Vector Decomposition","created":"2021-07-26","cited_by_count":9,"area":null},{"id":"2201.11676","title":"Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap","created":"2022-01-27","cited_by_count":9,"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":"2209.02764","title":"Change Detection for Local Explainability in Evolving Data Streams","created":"2022-09-06","cited_by_count":8,"area":null},{"id":"2306.12181","title":"Feature Interactions Reveal Linguistic Structure in Language Models","created":"2023-06-21","cited_by_count":8,"area":null},{"id":"2410.06300","title":"SHAP values via sparse Fourier representation","created":"2024-10-08","cited_by_count":8,"area":null},{"id":"2210.03047","title":"Conditional Feature Importance for Mixed Data","created":"2022-10-06","cited_by_count":7,"area":null},{"id":"2210.12369","title":"Explanation Shift: Detecting distribution shifts on tabular data via the explanation space","created":"2022-10-22","cited_by_count":7,"area":null},{"id":"2202.08815","title":"GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs","created":"2022-02-17","cited_by_count":6,"area":null},{"id":"2202.11919","title":"Threading the Needle of On and Off-Manifold Value Functions for Shapley Explanations","created":"2022-02-24","cited_by_count":6,"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":"2205.13662","title":"Explaining Preferences with Shapley Values","created":"2022-05-26","cited_by_count":6,"area":null},{"id":"2207.07038","title":"SHAP-XRT: The Shapley Value Meets Conditional Independence Testing","created":"2022-07-14","cited_by_count":6,"area":null},{"id":"2303.08081","title":"Explanation Shift: How Did the Distribution Shift Impact the Model?","created":"2023-03-14","cited_by_count":6,"area":null},{"id":"2111.11259","title":"Model-agnostic bias mitigation methods with regressor distribution control for Wasserstein-based fairness metrics","created":"2021-11-19","cited_by_count":5,"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":"2307.04850","title":"SHAP@k:Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features","created":"2023-07-10","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":"2406.11547","title":"GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations","created":"2024-06-17","cited_by_count":5,"area":null},{"id":"2410.04883","title":"Improving the Weighting Strategy in KernelSHAP","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2504.01842","title":"shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python","created":"2025-04-02","cited_by_count":5,"area":null},{"id":"2505.16516","title":"Amortized Linear-time Exact Shapley Value for Product-Kernel Methods","created":"2025-05-22","cited_by_count":5,"area":null},{"id":"2601.12654","title":"Explanation Multiplicity in SHAP: Characterization and Assessment","created":"2026-01-19","cited_by_count":5,"area":null},{"id":"2601.23068","title":"ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations","created":"2026-01-30","cited_by_count":5,"area":null},{"id":"2108.05390","title":"Seven challenges for harmonizing explainability requirements","created":"2021-08-11","cited_by_count":4,"area":null},{"id":"2304.03113","title":"Efficient SAGE Estimation via Causal Structure Learning","created":"2023-04-06","cited_by_count":4,"area":null},{"id":"2405.19212","title":"Partial Information Decomposition for Data Interpretability and Feature Selection","created":"2024-05-29","cited_by_count":4,"area":null},{"id":"2410.13448","title":"Fast Estimation of Partial Dependence Functions using Trees","created":"2024-10-17","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":"2103.13342","title":"The Shapley Value of coalition of variables provides better explanations","created":"2021-03-24","cited_by_count":3,"area":null},{"id":"2107.04010","title":"A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI","created":"2021-07-01","cited_by_count":3,"area":null},{"id":"2207.11231","title":"Learning Unsupervised Hierarchies of Audio Concepts","created":"2022-07-21","cited_by_count":3,"area":null},{"id":"2208.06096","title":"Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights","created":"2022-08-12","cited_by_count":3,"area":null},{"id":"2302.08434","title":"On marginal feature attributions of tree-based models","created":"2023-02-16","cited_by_count":3,"area":null},{"id":"2303.01181","title":"iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams","created":"2023-03-02","cited_by_count":3,"area":null},{"id":"2303.08040","title":"Beyond Demographic Parity: Redefining Equal Treatment","created":"2023-03-14","cited_by_count":3,"area":null},{"id":"2306.14672","title":"PWSHAP: A Path-Wise Explanation Model for Targeted Variables","created":"2023-06-26","cited_by_count":3,"area":null},{"id":"2401.11512","title":"TERC: A Transfer Entropy Redundancy Criterion for State Variable Selection in Reinforcement Learning","created":"2024-01-21","cited_by_count":3,"area":"deeprl"},{"id":"2505.04775","title":"Prediction via Shapley Value Regression","created":"2025-05-07","cited_by_count":3,"area":null},{"id":"2508.14667","title":"ELATE: Evolutionary Language model for Automated Time-series Engineering","created":"2025-08-20","cited_by_count":3,"area":null},{"id":"2511.09376","title":"From Decision Trees to Boolean Logic: A Fast and Unified SHAP Algorithm","created":"2025-11-12","cited_by_count":3,"area":null},{"id":"2109.02724","title":"Bringing a Ruler Into the Black Box: Uncovering Feature Impact from Individual Conditional Expectation Plots","created":"2021-09-06","cited_by_count":2,"area":null},{"id":"2202.09875","title":"Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variables","created":"2022-02-20","cited_by_count":2,"area":null},{"id":"2303.10216","title":"Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features","created":"2023-03-17","cited_by_count":2,"area":null},{"id":"2310.09961","title":"Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis","created":"2023-10-15","cited_by_count":2,"area":null},{"id":"2410.08336","title":"Kernel Banzhaf: A Fast and Robust Estimator for Banzhaf Values","created":"2024-10-10","cited_by_count":2,"area":null},{"id":"2412.01460","title":"A Comprehensive Study of Shapley Value in Data Analytics","created":"2024-12-02","cited_by_count":2,"area":null},{"id":"2501.12706","title":"REX: Causal discovery based on machine learning and explainability techniques","created":"2025-01-22","cited_by_count":2,"area":null},{"id":"2506.19894","title":"Explaining deep neural network models for electricity price forecasting with XAI","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2508.10490","title":"On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2508.10595","title":"On Spectral Properties of Gradient-based Explanation Methods","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2512.20514","title":"Explainable time-series forecasting with sampling-free SHAP for Transformers","created":"2025-12-23","cited_by_count":2,"area":null},{"id":"2109.03890","title":"Model Explanations via the Axiomatic Causal Lens","created":"2021-09-08","cited_by_count":1,"area":null},{"id":"2110.01421","title":"Unraveling the graph structure of tabular data through Bayesian and spectral analysis","created":"2021-10-04","cited_by_count":1,"area":null},{"id":"2301.00189","title":"Mapping Knowledge Representations to Concepts: A Review and New Perspectives","created":"2022-12-31","cited_by_count":1,"area":null},{"id":"2302.07733","title":"Explaining text classifiers through progressive neighborhood approximation with realistic samples","created":"2023-02-11","cited_by_count":1,"area":null},{"id":"2403.08880","title":"REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values","created":"2024-03-13","cited_by_count":1,"area":null},{"id":"2404.13522","title":"Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective","created":"2024-04-21","cited_by_count":1,"area":null},{"id":"2411.16251","title":"Transparent Neighborhood Approximation for Text Classifier Explanation","created":"2024-11-25","cited_by_count":1,"area":null},{"id":"2504.20687","title":"What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models","created":"2025-04-29","cited_by_count":1,"area":null},{"id":"2506.22443","title":"Learning Interpretable Rules from Neural Networks: Neurosymbolic AI for Radar Hand Gesture Recognition","created":"2025-06-11","cited_by_count":1,"area":null},{"id":"2601.22771","title":"GRANITE: A Generalized Regional Framework for Identifying Agreement in Feature-Based Explanations","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2602.16505","title":"Functional Decomposition and Shapley Interactions for Interpreting Survival Models","created":"2026-02-18","cited_by_count":1,"area":null},{"id":"2604.11223","title":"Regional Explanations: Bridging Local and Global Variable Importance","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2105.13787","title":"Do not explain without context: addressing the blind spot of model explanations","created":"2021-05-28","cited_by_count":0,"area":null},{"id":"2108.06201","title":"Data-driven advice for interpreting local and global model predictions in bioinformatics problems","created":"2021-08-13","cited_by_count":0,"area":null},{"id":"2206.00192","title":"Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models","created":"2022-06-01","cited_by_count":0,"area":null},{"id":"2206.08204","title":"Inherent Inconsistencies of Feature Importance","created":"2022-06-16","cited_by_count":0,"area":null},{"id":"2207.05214","title":"Shapley Computations Using Surrogate Model-Based Trees","created":"2022-07-11","cited_by_count":0,"area":null},{"id":"2211.06360","title":"Rethinking Log Odds: Linear Probability Modelling and Expert Advice in Interpretable Machine Learning","created":"2022-11-11","cited_by_count":0,"area":null},{"id":"2212.02906","title":"A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection","created":"2022-12-06","cited_by_count":0,"area":null},{"id":"2305.19428","title":"Evaluating geospatial context information for travel mode detection","created":"2023-05-30","cited_by_count":0,"area":null},{"id":"2306.10880","title":"Explaining the Model and Feature Dependencies by Decomposition of the Shapley Value","created":"2023-06-19","cited_by_count":0,"area":null}],"next_cursor":"100"}