mlsanityData to 7 Oct 2026

Strategic Classification Made Practical

arXiv 2103.01826 ↗First posted 2 Mar 2021cs.LG cs.GTcited by 58

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Abstract (arXiv)

Strategic classification regards the problem of learning in settings where users can strategically modify their features to improve outcomes. This setting applies broadly and has received much recent attention. But despite its practical significance, work in this space has so far been predominantly theoretical. In this paper we present a learning framework for strategic classification that is practical. Our approach directly minimizes the "strategic" empirical risk, achieved by differentiating through the strategic response of users. This provides flexibility that allows us to extend beyond the original problem formulation and towards more realistic learning scenarios. A series of experiments demonstrates the effectiveness of our approach on various learning settings.

Key facts. Cited by 58 papers in the map (data through 7 Oct 2026). Builds on 31 of its 50 references that are in the map. First posted on arXiv 2 Mar 2021.

Builds on

31 of 50 references.

References · 31

  1. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  2. 2017OptNet: Differentiable Optimization as a Layer in Neural Networkscited by 588
  3. 2019Differentiable Convex Optimization Layerscited by 458
  4. 2018Actionable Recourse in Linear Classificationcited by 327
  5. 2020Performative Predictioncited by 318
  6. 2015Strategic Classificationcited by 256
  7. 2020Algorithmic recourse under imperfect causal knowledge: a probabilistic approachcited by 139
  8. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 128
  9. 2017Strategic Classification from Revealed Preferencescited by 126
  10. 2020A survey of algorithmic recourse: definitions, formulations, solutions, and prospectscited by 118
  11. 2018The Social Cost of Strategic Classificationcited by 105
  12. 2021Outside the Echo Chamber: Optimizing the Performative Riskcited by 95
19 more
  1. 2019Strategic Classification is Causal Modeling in Disguisecited by 92
  2. 2020Performative Prediction in a Stateful Worldcited by 90
  3. 2018How Do Classifiers Induce Agents To Invest Effort Strategically?cited by 82
  4. 2020Learning with Differentiable Perturbed Optimizerscited by 81
  5. 2018The Disparate Effects of Strategic Manipulationcited by 75
  6. 2019Differentiating Through a Cone Programcited by 72
  7. 2021How to Learn when Data Reacts to Your Model: Performative Gradient Descentcited by 70
  8. 2021Strategic Classification in the Darkcited by 66
  9. 2020Causal Strategic Linear Regressioncited by 65
  10. 2019Equalizing Recourse across Groupscited by 51
  11. 2020PAC-Learning for Strategic Classificationcited by 50
  12. 2019On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learningcited by 48
  13. 2020The Strategic Perceptroncited by 48
  14. 2021Information Discrepancy in Strategic Learningcited by 48
  15. 2016Disciplined Convex-Concave Programmingcited by 29
  16. 2020From Predictions to Decisions: Using Lookahead Regularizationcited by 19
  17. 2020Learning Linear Programs from Optimal Decisionscited by 18
  18. 2020Gaming Helps! Learning from Strategic Interactions in Natural Dynamicscited by 14
  19. 2020Differentiating through Log-Log Convex Programscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 7; 2023: 9; 2024: 13; 2025: 15; 2026: 8.

58 papers.

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  1. 2021Who Leads and Who Follows in Strategic Classification?cited by 53
  2. 2021Alternative Microfoundations for Strategic Classificationcited by 47
  3. 2022Performative Powercited by 42
  4. 2022Generalized Strategic Classification and the Case of Aligned Incentivescited by 34
  5. 2023Causal Strategic Classification: A Tale of Two Shiftscited by 30
  6. 2021How to Learn when Data Gradually Reacts to Your Modelcited by 29
  7. 2021Auctions Between Regret-Minimizing Agentscited by 22
  8. 2023Plug-in Performative Optimizationcited by 20
  9. 2021How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agentscited by 17
  10. 2023Fundamental Bounds on Online Strategic Classificationcited by 17
  11. 2021Bayesian Persuasion for Algorithmic Recoursecited by 14
  12. 2022Strategic Classification with Graph Neural Networkscited by 14
38 more
  1. 2023One-Shot Strategic Classification Under Unknown Costscited by 13
  2. 2023Performative Recommendation: Diversifying Content via Strategic Incentivescited by 12
  3. 2024Non-linear Welfare-Aware Strategic Learningcited by 12
  4. 2023Strategic Apple Tastingcited by 10
  5. 2022Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instrumentscited by 9
  6. 2024Automating Data Annotation under Strategic Human Agents: Risks and Potential Solutionscited by 9
  7. 2022Addressing Strategic Manipulation Disparities in Fair Classificationcited by 8
  8. 2023The Risks of Recourse in Binary Classificationcited by 8
  9. 2025Performative Validity of Recourse Explanationscited by 8
  10. 2024Learning Decision Trees and Forests with Algorithmic Recoursecited by 7
  11. 2024Performative Prediction on Games and Mechanism Designcited by 7
  12. 2024Strategic Classification With Externalitiescited by 7
  13. 2024Learning In Reverse Causal Strategic Environments With Ramifications on Two Sided Marketscited by 6
  14. 2024Algorithmic Fairness in Performative Policy Learning: Escaping the Impossibility of Group Fairnesscited by 6
  15. 2025A Market for Accuracy: Classification under Competitioncited by 6
  16. 2023Algorithmic Censoring in Dynamic Learning Systemscited by 5
  17. 2024Learning the Distribution Map in Reverse Causal Performative Predictioncited by 5
  18. 2024Who's Gaming the System? A Causally-Motivated Approach for Detecting Strategic Adaptationcited by 5
  19. 2022Strategic Representationcited by 4
  20. 2022Strategyproof Decision-Making in Panel Data Settings and Beyondcited by 4
  21. 2023Prediction without Preclusion: Recourse Verification with Reachable Setscited by 4
  22. 2025How Strategic Agents Respond: Comparing Analytical Models with LLM-Generated Responses in Strategic Classificationcited by 4
  23. 2025Strategic Classification with Randomised Classifierscited by 4
  24. 2025Strategic Classification with Non-Linear Classifierscited by 4
  25. 2024Adversaries With Incentives: A Strategic Alternative to Adversarial Robustnesscited by 3
  26. 2024Practical Performative Policy Learning with Strategic Agentscited by 3
  27. 2025Learning Classifiers That Induce Marketscited by 3
  28. 2025Incentive-Aware Machine Learning; Robustness, Fairness, Improvement & Causalitycited by 3
  29. 2024Strategic Conformal Predictioncited by 2
  30. 2024Microfoundation Inference for Strategic Predictioncited by 2
  31. 2025Welfare as a Guiding Principle for Machine Learning -- From Compass, to Lens, to Roadmapcited by 2
  32. 2025To Give or Not to Give? The Impacts of Strategically Withheld Recoursecited by 2
  33. 2025Anticipating Gaming to Incentivize Improvement: Guiding Agents in (Fair) Strategic Classificationcited by 2
  34. 2025Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responsescited by 1
  35. 2025Understanding Endogenous Data Drift in Adaptive Models with Recourse-Seeking Userscited by 1
  36. 2025Strategic Filtering for Content Moderation: Free Speech or Free of Distortion?cited by 1
  37. 2026Beyond Rational Illusion: Behaviorally Realistic Strategic Classificationcited by 1
  38. 2026The Role of Causal Features in Strategic Classification for Robustness and Alignmentcited by 1

50 most cited of 58 All 58 (JSON)