mlsanityData to 7 Oct 2026

R'enyi Fair Inference

arXiv 1906.12005 ↗First posted 28 Jun 2019cs.LG stat.MLcited by 33

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Abstract (arXiv)

Machine learning algorithms have been increasingly deployed in critical automated decision-making systems that directly affect human lives. When these algorithms are only trained to minimize the training/test error, they could suffer from systematic discrimination against individuals based on their sensitive attributes such as gender or race. Recently, there has been a surge in machine learning society to develop algorithms for fair machine learning. In particular, many adversarial learning procedures have been proposed to impose fairness. Unfortunately, these algorithms either can only impose fairness up to first-order dependence between the variables, or they lack computational convergence guarantees. In this paper, we use R'enyi correlation as a measure of fairness of machine learning models and develop a general training framework to impose fairness. In particular, we propose a min-max formulation which balances the accuracy and fairness when solved to optimality. For the case of discrete sensitive attributes, we suggest an iterative algorithm with theoretical convergence guarantee for solving the proposed min-max problem. Our algorithm and analysis are then specialized to fair classification and the fair clustering problem under disparate impact doctrine. Finally, the performance of the proposed R'enyi fair inference framework is evaluated on Adult and Bank datasets.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2019.

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Citing papers per year: 2021: 6; 2022: 8; 2023: 14; 2024: 3; 2025: 2.

33 papers.

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  1. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 42
  2. 2022Fairness Reprogrammingcited by 33
  3. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  4. 2022Understanding Instance-Level Impact of Fairness Constraintscited by 27
  5. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  6. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  7. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  8. 2022Proportional Fairness in Federated Learningcited by 20
  9. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
  10. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  11. 2022Stochastic Differentially Private and Fair Learningcited by 16
  12. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
21 more
  1. 2021Wasserstein Robust Classification with Fairness Constraintscited by 10
  2. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  3. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  4. 2021Fairness Through Regularization for Learning to Rankcited by 8
  5. 2021Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domaincited by 8
  6. 2023A Multidimensional Analysis of Social Biases in Vision Transformerscited by 8
  7. 2022A Fair Pricing Model via Adversarial Learningcited by 7
  8. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  9. 2023PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexitiescited by 5
  10. 2024On the Inductive Biases of Demographic Parity-based Fair Learning Algorithmscited by 5
  11. 2023f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimizationcited by 4
  12. 2023Enhancing Group Fairness in Online Settings Using Oblique Decision Forestscited by 3
  13. 2023Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testingcited by 3
  14. 2024A Unified View of Group Fairness Tradeoffs Using Partial Information Decompositioncited by 3
  15. 2024Different Horses for Different Courses: Comparing Bias Mitigation Algorithms in MLcited by 3
  16. 2022Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measurescited by 1
  17. 2023Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classificationcited by 1
  18. 2023Boosting Fair Classifier Generalization through Adaptive Priority Reweighingcited by 1
  19. 2023Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Frameworkcited by 1
  20. 2025pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learningcited by 1
  21. 2025Toward Unifying Group Fairness Evaluation from a Sparsity Perspectivecited by 0