{"total":99,"rows":[{"id":"2110.01889","title":"Deep Neural Networks and Tabular Data: A Survey","created":"2021-10-05","cited_by_count":491,"area":null},{"id":"2206.11104","title":"OpenXAI: Towards a Transparent Evaluation of Model Explanations","created":"2022-06-22","cited_by_count":126,"area":null},{"id":"2112.11313","title":"On the Adversarial Robustness of Causal Algorithmic Recourse","created":"2021-12-21","cited_by_count":56,"area":null},{"id":"2207.02739","title":"Robust Counterfactual Explanations for Tree-Based Ensembles","created":"2022-07-06","cited_by_count":49,"area":null},{"id":"2305.11997","title":"Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees","created":"2023-05-19","cited_by_count":45,"area":null},{"id":"2203.06768","title":"Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse","created":"2022-03-13","cited_by_count":40,"area":null},{"id":"2111.00358","title":"A Survey on the Robustness of Feature Importance and Counterfactual Explanations","created":"2021-10-30","cited_by_count":39,"area":null},{"id":"2402.01928","title":"Robust Counterfactual Explanations in Machine Learning: A Survey","created":"2024-02-02","cited_by_count":29,"area":null},{"id":"2305.17021","title":"GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations","created":"2023-05-26","cited_by_count":27,"area":null},{"id":"2208.14878","title":"Formalising the Robustness of Counterfactual Explanations for Neural Networks","created":"2022-08-31","cited_by_count":24,"area":null},{"id":"2205.15834","title":"Attribution-based Explanations that Provide Recourse Cannot be Robust","created":"2022-05-31","cited_by_count":20,"area":null},{"id":"2208.14137","title":"On the Trade-Off between Actionable Explanations and the Right to be Forgotten","created":"2022-08-30","cited_by_count":20,"area":null},{"id":"2210.15709","title":"Improvement-Focused Causal Recourse (ICR)","created":"2022-10-27","cited_by_count":20,"area":null},{"id":"2309.12545","title":"Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation","created":"2023-09-22","cited_by_count":20,"area":null},{"id":"2201.12487","title":"Counterfactual Plans under Distributional Ambiguity","created":"2022-01-29","cited_by_count":19,"area":null},{"id":"2301.11113","title":"Finding Regions of Counterfactual Explanations via Robust Optimization","created":"2023-01-26","cited_by_count":19,"area":null},{"id":"2206.10833","title":"Robust Bayesian Recourse","created":"2022-06-22","cited_by_count":18,"area":null},{"id":"2109.07557","title":"CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations","created":"2021-09-15","cited_by_count":17,"area":null},{"id":"2312.06564","title":"Promoting Counterfactual Robustness through Diversity","created":"2023-12-11","cited_by_count":17,"area":null},{"id":"2107.09734","title":"Uncertainty Estimation and Out-of-Distribution Detection for Counterfactual Explanations: Pitfalls and Solutions","created":"2021-07-20","cited_by_count":16,"area":null},{"id":"2312.15097","title":"Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)","created":"2023-12-22","cited_by_count":15,"area":null},{"id":"2405.05369","title":"Model Reconstruction Using Counterfactual Explanations: A Perspective From Polytope Theory","created":"2024-05-08","cited_by_count":14,"area":null},{"id":"2304.12943","title":"Generating robust counterfactual explanations","created":"2023-04-24","cited_by_count":13,"area":null},{"id":"2309.06969","title":"Setting the Right Expectations: Algorithmic Recourse Over Time","created":"2023-09-13","cited_by_count":13,"area":null},{"id":"2302.03465","title":"Robustness Implies Fairness in Causal Algorithmic Recourse","created":"2023-02-07","cited_by_count":12,"area":null},{"id":"2302.11211","title":"Distributionally Robust Recourse Action","created":"2023-02-22","cited_by_count":12,"area":null},{"id":"2206.00700","title":"RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model","created":"2022-06-01","cited_by_count":11,"area":null},{"id":"2111.01235","title":"Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions","created":"2021-11-01","cited_by_count":10,"area":null},{"id":"2302.04288","title":"Towards Bridging the Gaps between the Right to Explanation and the Right to be Forgotten","created":"2023-02-08","cited_by_count":10,"area":null},{"id":"2408.04842","title":"Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Change","created":"2024-08-09","cited_by_count":10,"area":null},{"id":"2308.08187","title":"Endogenous Macrodynamics in Algorithmic Recourse","created":"2023-08-16","cited_by_count":9,"area":null},{"id":"2401.16088","title":"Fairness in Algorithmic Recourse Through the Lens of Substantive Equality of Opportunity","created":"2024-01-29","cited_by_count":9,"area":null},{"id":"2410.08007","title":"Time Can Invalidate Algorithmic Recourse","created":"2024-10-10","cited_by_count":8,"area":null},{"id":"2410.22598","title":"Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse","created":"2024-10-29","cited_by_count":8,"area":null},{"id":"2502.06749","title":"Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertainty","created":"2025-02-10","cited_by_count":8,"area":null},{"id":"2506.15366","title":"Performative Validity of Recourse Explanations","created":"2025-06-18","cited_by_count":8,"area":null},{"id":"2404.13736","title":"Interval Abstractions for Robust Counterfactual Explanations","created":"2024-04-21","cited_by_count":7,"area":null},{"id":"2206.12481","title":"Analyzing Explainer Robustness via Probabilistic Lipschitzness of Prediction Functions","created":"2022-06-24","cited_by_count":6,"area":null},{"id":"2302.11213","title":"Feasible Recourse Plan via Diverse Interpolation","created":"2023-02-22","cited_by_count":6,"area":null},{"id":"2309.04676","title":"Flexible and Robust Counterfactual Explanations with Minimal Satisfiable Perturbations","created":"2023-09-09","cited_by_count":6,"area":null},{"id":"2407.07482","title":"Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations","created":"2024-07-10","cited_by_count":6,"area":null},{"id":"2502.13751","title":"RobustX: Robust Counterfactual Explanations Made Easy","created":"2025-02-19","cited_by_count":6,"area":null},{"id":"2305.09035","title":"Algorithmic Censoring in Dynamic Learning Systems","created":"2023-05-15","cited_by_count":5,"area":null},{"id":"2310.01684","title":"Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations","created":"2023-10-02","cited_by_count":5,"area":null},{"id":"2312.10648","title":"Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals","created":"2023-12-17","cited_by_count":5,"area":null},{"id":"2403.05652","title":"\"What is Different Between These Datasets?\" A Framework for Explaining Data Distribution Shifts","created":"2024-03-08","cited_by_count":5,"area":null},{"id":"2503.07934","title":"Counterfactual Explanations for Model Ensembles Using Entropic Risk Measures","created":"2025-03-11","cited_by_count":5,"area":null},{"id":"2505.07351","title":"From Search To Sampling: Generative Models For Robust Algorithmic Recourse","created":"2025-05-12","cited_by_count":5,"area":null},{"id":"2308.07198","title":"Explaining Black-Box Models through Counterfactuals","created":"2023-08-14","cited_by_count":4,"area":null},{"id":"2308.12820","title":"Prediction without Preclusion: Recourse Verification with Reachable Sets","created":"2023-08-24","cited_by_count":4,"area":null},{"id":"2410.01580","title":"Learning-Augmented Robust Algorithmic Recourse","created":"2024-10-02","cited_by_count":4,"area":null},{"id":"2206.10697","title":"Performance Prediction Under Dataset Shift","created":"2022-06-21","cited_by_count":3,"area":null},{"id":"2306.06716","title":"On Minimizing the Impact of Dataset Shifts on Actionable Explanations","created":"2023-06-11","cited_by_count":3,"area":null},{"id":"2402.19027","title":"How to Train your Antivirus: RL-based Hardening through the Problem-Space","created":"2024-02-29","cited_by_count":3,"area":null},{"id":"2501.05795","title":"Robust Counterfactual Explanations under Model Multiplicity Using Multi-Objective Optimization","created":"2025-01-10","cited_by_count":3,"area":null},{"id":"2509.21293","title":"Optimal Robust Recourse with $L^p$-Bounded Model Change","created":"2025-09-25","cited_by_count":3,"area":null},{"id":"2205.14116","title":"Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles","created":"2022-05-27","cited_by_count":2,"area":null},{"id":"2306.06193","title":"Consistent Explanations in the Face of Model Indeterminacy via Ensembling","created":"2023-06-09","cited_by_count":2,"area":null},{"id":"2403.03773","title":"Verified Training for Counterfactual Explanation Robustness under Data Shift","created":"2024-03-06","cited_by_count":2,"area":null},{"id":"2407.11426","title":"Generally-Occurring Model Change for Robust Counterfactual Explanations","created":"2024-07-16","cited_by_count":2,"area":null},{"id":"2410.02273","title":"Perfect Counterfactuals in Imperfect Worlds: Modelling Noisy Implementation of Actions in Sequential Algorithmic Recourse","created":"2024-10-03","cited_by_count":2,"area":null},{"id":"2410.13812","title":"Private Counterfactual Retrieval","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2410.14640","title":"HR-Bandit: Human-AI Collaborated Linear Recourse Bandit","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2411.01126","title":"Axiomatic Explainer Globalness via Optimal Transport","created":"2024-11-02","cited_by_count":2,"area":null},{"id":"2602.07674","title":"ElliCE: Efficient and Provably Robust Algorithmic Recourse via the Rashomon Sets","created":"2026-02-07","cited_by_count":2,"area":null},{"id":"2212.14106","title":"Provable Robust Saliency-based Explanations","created":"2022-12-28","cited_by_count":1,"area":null},{"id":"2303.11111","title":"Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks","created":"2023-03-17","cited_by_count":1,"area":null},{"id":"2308.12367","title":"SafeAR: Safe Algorithmic Recourse by Risk-Aware Policies","created":"2023-08-23","cited_by_count":1,"area":null},{"id":"2402.04579","title":"Collective Counterfactual Explanations: Balancing Individual Goals and Collective Dynamics","created":"2024-02-07","cited_by_count":1,"area":null},{"id":"2502.16380","title":"Understanding Fixed Predictions via Confined Regions","created":"2025-02-22","cited_by_count":1,"area":null},{"id":"2503.09658","title":"Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Users","created":"2025-03-12","cited_by_count":1,"area":null},{"id":"2506.20260","title":"Argumentative Ensembling for Robust Recourse under Model Multiplicity","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2510.04855","title":"Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders","created":"2025-10-06","cited_by_count":1,"area":null},{"id":"2601.16659","title":"Provably Robust Bayesian Counterfactual Explanations under Model Changes","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2604.08030","title":"From Universal to Individualized Actionability: Revisiting Personalization in Algorithmic Recourse","created":"2026-04-09","cited_by_count":1,"area":null},{"id":"2606.18889","title":"Improving Medical Communication using Rubric-Guided Counterfactual Recommendations","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2607.28497","title":"The Role of Causality in Algorithmic Recourse","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2307.04024","title":"Robust Ranking Explanations","created":"2023-07-08","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":"2309.08332","title":"Estimation of Counterfactual Interventions under Uncertainties","created":"2023-09-15","cited_by_count":0,"area":null},{"id":"2309.16452","title":"On the Trade-offs between Adversarial Robustness and Actionable Explanations","created":"2023-09-28","cited_by_count":0,"area":null},{"id":"2312.08724","title":"Personalized Path Recourse for Reinforcement Learning Agents","created":"2023-12-14","cited_by_count":0,"area":null},{"id":"2402.15073","title":"Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation","created":"2024-02-23","cited_by_count":0,"area":null},{"id":"2403.14467","title":"Recourse for reclamation: Chatting with generative language models","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2405.18671","title":"Watermarking Counterfactual Explanations","created":"2024-05-29","cited_by_count":0,"area":null},{"id":"2411.10429","title":"Private Counterfactual Retrieval With Immutable Features","created":"2024-11-15","cited_by_count":0,"area":null},{"id":"2512.10878","title":"Classifier Reconstruction Through Counterfactual-Aware Wasserstein Prototypes","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.11905","title":"LIBRA: Language Model Informed Bandit Recourse Algorithm for Personalized Treatment Planning","created":"2026-01-17","cited_by_count":0,"area":null},{"id":"2602.00063","title":"The Impact of Machine Learning Uncertainty on the Robustness of Counterfactual Explanations","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2604.17494","title":"A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanations","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2605.16041","title":"Explainable AI Isn't Enough! Rethinking Algorithmic Contestability","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2605.17160","title":"When Bits Break Recourse: Counterfactual-Faithful Quantization","created":"2026-05-16","cited_by_count":0,"area":null},{"id":"2605.31272","title":"Algorithmic Recourse of In-Context Learning for Tabular Data","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.16113","title":"RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2606.18832","title":"Target-confidence Recourse Using tSeTlin machines: TRUST","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2606.27948","title":"RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data","created":"2026-06-26","cited_by_count":0,"area":null},{"id":"2608.30956","title":"Taking the Whys Seriously: Limitations of Counterfactual Explanations in Justification and Recourse","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.30918","title":"Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanations","created":"2026-09-25","cited_by_count":0,"area":null},{"id":"2609.39877","title":"Algorithmic Recourse Under Competition","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}