{"total":88,"rows":[{"id":"2107.07436","title":"FastSHAP: Real-Time Shapley Value Estimation","created":"2021-07-15","cited_by_count":125,"area":null},{"id":"2202.05594","title":"The Shapley Value in Machine Learning","created":"2022-02-11","cited_by_count":98,"area":null},{"id":"2412.02104","title":"Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey","created":"2024-12-03","cited_by_count":52,"area":"mllm"},{"id":"2206.05282","title":"Learning to Estimate Shapley Values with Vision Transformers","created":"2022-06-10","cited_by_count":45,"area":null},{"id":"2410.01649","title":"shapiq: Shapley Interactions for Machine Learning","created":"2024-10-02","cited_by_count":43,"area":null},{"id":"2209.13429","title":"WeightedSHAP: analyzing and improving Shapley based feature attributions","created":"2022-09-27","cited_by_count":36,"area":null},{"id":"2202.10553","title":"Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis","created":"2022-02-16","cited_by_count":35,"area":null},{"id":"2302.08160","title":"The Inadequacy of Shapley Values for Explainability","created":"2023-02-16","cited_by_count":31,"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":"2306.05810","title":"Explaining Reinforcement Learning with Shapley Values","created":"2023-06-09","cited_by_count":26,"area":null},{"id":"2110.14270","title":"Counterfactual Shapley Additive Explanations","created":"2021-10-27","cited_by_count":22,"area":null},{"id":"2303.02794","title":"CoRTX: Contrastive Framework for Real-time Explanation","created":"2023-03-05","cited_by_count":20,"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":"2306.07462","title":"On the Robustness of Removal-Based Feature Attributions","created":"2023-06-12","cited_by_count":19,"area":null},{"id":"2206.07387","title":"The Manifold Hypothesis for Gradient-Based Explanations","created":"2022-06-15","cited_by_count":16,"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":"2410.11087","title":"Locality Alignment Improves Vision-Language Models","created":"2024-10-14","cited_by_count":13,"area":"lr"},{"id":"2501.16944","title":"Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks","created":"2025-01-28","cited_by_count":13,"area":null},{"id":"2111.03549","title":"Interpreting Representation Quality of DNNs for 3D Point Cloud Processing","created":"2021-11-05","cited_by_count":12,"area":null},{"id":"2302.12893","title":"Don't be fooled: label leakage in explanation methods and the importance of their quantitative evaluation","created":"2023-02-24","cited_by_count":11,"area":null},{"id":"2105.10719","title":"Can We Faithfully Represent Masked States to Compute Shapley Values on a DNN?","created":"2021-05-22","cited_by_count":10,"area":null},{"id":"2206.00759","title":"Interpretability Guarantees with Merlin-Arthur Classifiers","created":"2022-06-01","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":"2301.04041","title":"Manifold Restricted Interventional Shapley Values","created":"2023-01-10","cited_by_count":9,"area":null},{"id":"2408.04643","title":"Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey","created":"2024-08-01","cited_by_count":8,"area":null},{"id":"2106.06528","title":"Local Explanation of Dialogue Response Generation","created":"2021-06-11","cited_by_count":7,"area":null},{"id":"2205.15419","title":"Fool SHAP with Stealthily Biased Sampling","created":"2022-05-30","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":"2404.00673","title":"A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures","created":"2024-03-31","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":"2205.15750","title":"Variable importance without impossible data","created":"2022-05-31","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":"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":"2602.01399","title":"An Odd Estimator for Shapley Values","created":"2026-02-01","cited_by_count":5,"area":null},{"id":"2304.03113","title":"Efficient SAGE Estimation via Causal Structure Learning","created":"2023-04-06","cited_by_count":4,"area":null},{"id":"2306.07775","title":"iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios","created":"2023-06-13","cited_by_count":4,"area":null},{"id":"2309.03041","title":"A Refutation of Shapley Values for Explainability","created":"2023-09-06","cited_by_count":4,"area":null},{"id":"2506.05216","title":"A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values","created":"2025-06-05","cited_by_count":4,"area":null},{"id":"2602.11623","title":"TreeGrad-Ranker: Feature Ranking via $O(L)$-Time Gradients for Decision Trees","created":"2026-02-12","cited_by_count":4,"area":null},{"id":"2202.11797","title":"Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four","created":"2022-02-23","cited_by_count":3,"area":null},{"id":"2208.10228","title":"Review of Natural Language Processing in Pharmacology","created":"2022-08-22","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":"2310.04821","title":"A New Baseline Assumption of Integated Gradients Based on Shaply value","created":"2023-10-07","cited_by_count":3,"area":null},{"id":"2402.09947","title":"Explaining Probabilistic Models with Distributional Values","created":"2024-02-15","cited_by_count":3,"area":null},{"id":"2505.04775","title":"Prediction via Shapley Value Regression","created":"2025-05-07","cited_by_count":3,"area":null},{"id":"2511.10439","title":"Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration","created":"2025-11-13","cited_by_count":3,"area":null},{"id":"2211.13289","title":"Shapley Curves: A Smoothing Perspective","created":"2022-11-23","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":"2311.01010","title":"Fast Shapley Value Estimation: A Unified Approach","created":"2023-11-02","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":"2503.06602","title":"FW-Shapley: Real-time Estimation of Weighted Shapley Values","created":"2025-03-09","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":"2602.09326","title":"Priority-Aware Shapley Value","created":"2026-02-10","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":"2212.03131","title":"Explainability as statistical inference","created":"2022-12-06","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":"2303.17243","title":"Shapley Chains: Extending Shapley Values to Classifier Chains","created":"2023-03-30","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":"2401.03128","title":"Manifold-based Shapley for SAR Recognization Network Explanation","created":"2024-01-06","cited_by_count":1,"area":null},{"id":"2404.01078","title":"Energy-Based Model for Accurate Estimation of Shapley Values in Feature Attribution","created":"2024-04-01","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":"2406.02778","title":"MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning","created":"2024-06-04","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":"2509.00846","title":"Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery","created":"2025-08-31","cited_by_count":1,"area":null},{"id":"2510.19244","title":"Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments","created":"2025-10-22","cited_by_count":1,"area":"deeprl"},{"id":"2511.06094","title":"Approximating Shapley Explanations in Reinforcement Learning","created":"2025-11-08","cited_by_count":1,"area":"deeprl"},{"id":"2602.09489","title":"Computing Conditional Shapley Values Using Tabular Foundation Models","created":"2026-02-10","cited_by_count":1,"area":null},{"id":"2603.04831","title":"Missingness Bias Calibration in Feature Attribution Explanations","created":"2026-03-05","cited_by_count":1,"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":"2207.05214","title":"Shapley Computations Using Surrogate Model-Based Trees","created":"2022-07-11","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},{"id":"2307.06941","title":"On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations","created":"2023-07-13","cited_by_count":0,"area":null},{"id":"2311.06192","title":"Greedy PIG: Adaptive Integrated Gradients","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2311.13454","title":"Explaining high-dimensional text classifiers","created":"2023-11-22","cited_by_count":0,"area":null},{"id":"2408.12420","title":"Dataset | Mindset = Explainable AI | Interpretable AI","created":"2024-08-22","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":"2505.09660","title":"On Measuring Intrinsic Causal Attributions in Deep Neural Networks","created":"2025-05-14","cited_by_count":0,"area":null},{"id":"2506.01450","title":"ShaTS: A Shapley-based Explainability Method for Time Series Artificial Intelligence Models applied to Anomaly Detection in Industrial Internet of Things","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2506.19630","title":"Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2508.15652","title":"Understanding Action Effects through Instrumental Empowerment in Multi-Agent Reinforcement Learning","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2509.04009","title":"Token-Based Detection of Spurious Correlations in Vision Transformers","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2603.05093","title":"From Baselines to Transport Geodesics: Axiomatic Attribution via Optimal Generative Flows","created":"2026-03-05","cited_by_count":0,"area":null},{"id":"2606.02247","title":"ShaplEIG: Bayesian Experimental Design for Shapley Value Estimation","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2606.16786","title":"We Need Explanation Cards to Connect Explanation Algorithms to the Real World","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2607.14271","title":"Local Additive Feature Attribution: A Mathematical Taxonomy and Reporting Checklist","created":"2026-07-15","cited_by_count":0,"area":null},{"id":"2608.11508","title":"RelShap: Relationally Consistent Shapley Explanations","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2608.12935","title":"Decomposition of Evidence, Contradiction, and Fragility in Perturbation Responses","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.35217","title":"A Hierarchy of Entropy-Shapley Games for Multivariate Predictive Uncertainty","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}