mlsanityData to 7 Oct 2026

Learning-Augmented Robust Algorithmic Recourse

arXiv 2410.01580 ↗First posted 2 Oct 2024cs.LGcited by 4

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Abstract (arXiv)

Algorithmic recourse provides individuals who receive undesirable outcomes from machine learning systems with minimum-cost improvements to achieve a desirable outcome. However, machine learning models often get updated, so the recourse may not lead to the desired outcome. The robust recourse framework chooses recourses that are less sensitive to adversarial model changes, but this comes at a higher cost. To address this, we initiate the study of learning-augmented algorithmic recourse and evaluate the extent to which a designer equipped with a prediction of the future model can reduce the cost of recourse when the prediction is accurate (consistency) while also limiting the cost even when the prediction is inaccurate (robustness). We propose a novel algorithm, study the robustness-consistency trade-off, and analyze how prediction accuracy affects performance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 62 of its 90 references that are in the map. First posted on arXiv 2 Oct 2024.

Builds on

62 of 90 references.

References · 62

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  6. 2021Convex optimizationcited by 2,479
  7. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  8. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  9. 2018Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examplescited by 1,259
  10. 2017SmoothGrad: removing noise by adding noisecited by 1,097
  11. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,086
  12. 2017Provable defenses against adversarial examples via the convex outer adversarial polytopecited by 564
38 more
  1. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  2. 2018Actionable Recourse in Linear Classificationcited by 327
  3. 2020Performative Predictioncited by 318
  4. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  5. 2019Interpretable Counterfactual Explanations Guided by Prototypescited by 223
  6. 2019Model-Agnostic Counterfactual Explanations for Consequential Decisionscited by 204
  7. 2018Competitive caching with machine learned advicecited by 194
  8. 2017The Case for Learned Index Structurescited by 188
  9. 2019Predictive Multiplicity in Classificationcited by 162
  10. 2020Algorithms with Predictionscited by 152
  11. 2019Learning Model-Agnostic Counterfactual Explanations for Tabular Datacited by 146
  12. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  13. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 128
  14. 2021On the Convergence and Robustness of Adversarial Trainingcited by 127
  15. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  16. 2021Counterfactual Explanations Can Be Manipulatedcited by 113
  17. 2021Towards Robust and Reliable Algorithmic Recoursecited by 101
  18. 2022Exploring the Whole Rashomon Set of Sparse Decision Treescited by 80
  19. 2024Improving Online Algorithms via ML Predictionscited by 65
  20. 2016Scalable Bayesian Rule Listscited by 64
  21. 2021On the Adversarial Robustness of Causal Algorithmic Recoursecited by 56
  22. 2021Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysiscited by 52
  23. 2019Equalizing Recourse across Groupscited by 51
  24. 2022Robust Counterfactual Explanations for Tree-Based Ensemblescited by 49
  25. 2019On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learningcited by 48
  26. 2021Information Discrepancy in Strategic Learningcited by 48
  27. 2021Towards the Unification and Robustness of Perturbation and Gradient Based Explanationscited by 47
  28. 2023Robust Counterfactual Explanations for Neural Networks With Probabilistic Guaranteescited by 45
  29. 2022Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recoursecited by 40
  30. 2021Consistent Counterfactuals for Deep Modelscited by 37
  31. 2024Robust Counterfactual Explanations in Machine Learning: A Surveycited by 29
  32. 2020Efficient Training of Robust Decision Trees Against Adversarial Examplescited by 24
  33. 2022On the Trade-Off between Actionable Explanations and the Right to be Forgottencited by 20
  34. 2022Learning-Augmented Mechanism Design: Leveraging Predictions for Facility Locationcited by 19
  35. 2022Robust Bayesian Recoursecited by 18
  36. 2023Promoting Counterfactual Robustness through Diversitycited by 17
  37. 2023Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)cited by 15
  38. 2019On Robustness to Adversarial Examples and Polynomial Optimizationcited by 14

50 most cited of 62 All 62 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Optimal Robust Recourse with Lp-Bounded Model Changecited by 3
  2. 2026The Role of Causality in Algorithmic Recoursecited by 1
  3. 2026RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluationcited by 0
  4. 2026Algorithmic Recourse Under Competitioncited by 0