mlsanityData to 7 Oct 2026

Robust Bayesian Recourse

arXiv 2206.10833 ↗First posted 22 Jun 2022cs.LGcited by 18

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Abstract (arXiv)

Algorithmic recourse aims to recommend an informative feedback to overturn an unfavorable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnostic recourse that minimizes the posterior probability odds ratio. Further, we present its min-max robust counterpart with the goal of hedging against future changes in the machine learning model parameters. The robust counterpart explicitly takes into account possible perturbations of the data in a Gaussian mixture ambiguity set prescribed using the optimal transport (Wasserstein) distance. We show that the resulting worst-case objective function can be decomposed into solving a series of two-dimensional optimization subproblems, and the min-max recourse finding problem is thus amenable to a gradient descent algorithm. Contrary to existing methods for generating robust recourses, the robust Bayesian recourse does not require a linear approximation step. The numerical experiment demonstrates the effectiveness of our proposed robust Bayesian recourse facing model shifts. Our code is available at https://github.com/VinAIResearch/robust-bayesian-recourse.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 29 of its 56 references that are in the map. First posted on arXiv 22 Jun 2022.

Builds on

29 of 56 references.

References · 29

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  2. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,324
  3. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  4. 2019Explaining Machine Learning Classifiers through Diverse Counterfactual Explanationscited by 612
  5. 2019Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methodscited by 440
  6. 2018Actionable Recourse in Linear Classificationcited by 327
  7. 2018On the Robustness of Interpretability Methodscited by 310
  8. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 232
  9. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  10. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  11. 2021CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithmscited by 85
  12. 2019A Wasserstein-type distance in the space of Gaussian Mixture Modelscited by 75
17 more
  1. 2020On Counterfactual Explanations under Predictive Multiplicitycited by 73
  2. 2020GANterfactual - Counterfactual Explanations for Medical Non-Experts using Generative Adversarial Learningcited by 50
  3. 2019Measurable Counterfactual Local Explanations for Any Classifiercited by 48
  4. 2021Towards the Unification and Robustness of Perturbation and Gradient Based Explanationscited by 47
  5. 2017Optimal transport for Gaussian mixture modelscited by 45
  6. 2021A Survey on the Robustness of Feature Importance and Counterfactual Explanationscited by 38
  7. 2020Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shiftscited by 34
  8. 2018Defining Locality for Surrogates in Post-hoc Interpretablitycited by 33
  9. 2019On the computation of counterfactual explanations -- A surveycited by 33
  10. 2022Counterfactual Plans under Distributional Ambiguitycited by 19
  11. 2021Sequential Domain Adaptation by Synthesizing Distributionally Robust Expertscited by 14
  12. 2019Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximationcited by 12
  13. 2022Distributionally Robust Fair Principal Components via Geodesic Descentscited by 9
  14. 2019Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimizationcited by 7
  15. 2020Explaining Predictions by Approximating the Local Decision Boundarycited by 7
  16. 2020Robust Bayesian Classification Using an Optimistic Score Ratiocited by 6
  17. 2022Margin-distancing for safe model explanationcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 6; 2025: 2; 2026: 5.

18 papers.

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  1. 2024Robust Counterfactual Explanations in Machine Learning: A Surveycited by 29
  2. 2023Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisationcited by 20
  3. 2023Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)cited by 15
  4. 2023Distributionally Robust Recourse Actioncited by 12
  5. 2024Counterfactual Explanations with Probabilistic Guarantees on their Robustness to Model Changecited by 10
  6. 2024Interval Abstractions for Robust Counterfactual Explanationscited by 7
  7. 2023Bayesian Hierarchical Models for Counterfactual Estimationcited by 6
  8. 2024Rigorous Probabilistic Guarantees for Robust Counterfactual Explanationscited by 6
  9. 2024Learning-Augmented Robust Algorithmic Recoursecited by 4
  10. 2025Counterfactual Explanations for Model Ensembles Using Entropic Risk Measurescited by 4
  11. 2023On Minimizing the Impact of Dataset Shifts on Actionable Explanationscited by 3
  12. 2025Optimal Robust Recourse with Lp-Bounded Model Changecited by 3
6 more
  1. 2024Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitationcited by 0
  2. 2026A Probabilistic Consensus-Driven Approach for Robust Counterfactual Explanationscited by 0
  3. 2026RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluationcited by 0
  4. 2026A Generalized-Bayes Perspective on Counterfactual Explanations: Posterior-Based Decision-Making and Evaluationcited by 0
  5. 2026Robust to Which Model Change? A Unified Evaluation of Robust Counterfactual Explanationscited by 0
  6. 2026Algorithmic Recourse Under Competitioncited by 0