mlsanityData to 7 Oct 2026

Toward a better trade-off between performance and fairness with kernel-based distribution matching

arXiv 1910.11779 ↗First posted 25 Oct 2019cs.LG stat.MLcited by 36

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Abstract (arXiv)

As recent literature has demonstrated how classifiers often carry unintended biases toward some subgroups, deploying machine learned models to users demands careful consideration of the social consequences. How should we address this problem in a real-world system? How should we balance core performance and fairness metrics? In this paper, we introduce a MinDiff framework for regularizing classifiers toward different fairness metrics and analyze a technique with kernel-based statistical dependency tests. We run a thorough study on an academic dataset to compare the Pareto frontier achieved by different regularization approaches, and apply our kernel-based method to two large-scale industrial systems demonstrating real-world improvements.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Oct 2019.

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Citing papers per year: 2021: 10; 2022: 7; 2023: 11; 2024: 5; 2025: 3.

36 papers.

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  1. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 890
  2. 2021Trustworthy AI: A Computational Perspectivecited by 93
  3. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 42
  4. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  5. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  6. 2021Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Frameworkcited by 19
  7. 2021Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifoldcited by 18
  8. 2022Stochastic Differentially Private and Fair Learningcited by 16
  9. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  10. 2021InfoFair: Information-Theoretic Intersectional Fairnesscited by 15
  11. 2023A Survey on Fairness-aware Recommender Systemscited by 14
  12. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
24 more
  1. 2022How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shiftscited by 11
  2. 2022Fairness and robustness in anti-causal predictioncited by 10
  3. 2024The Role of Learning Algorithms in Collective Actioncited by 8
  4. 2022Malign Overfitting: Interpolation Can Provably Preclude Invariancecited by 6
  5. 2023Deceptive Fairness Attacks on Graphs via Meta Learningcited by 6
  6. 2022Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluationscited by 5
  7. 2024On the Inductive Biases of Demographic Parity-based Fair Learning Algorithmscited by 5
  8. 2022Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Talecited by 4
  9. 2023f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimizationcited by 4
  10. 2023Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Accesscited by 3
  11. 2023Enhancing Group Fairness in Online Settings Using Oblique Decision Forestscited by 3
  12. 2024Inducing Group Fairness in Prompt-Based Language Model Decisionscited by 3
  13. 2021Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysiscited by 2
  14. 2023FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Softwarecited by 2
  15. 2024Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributescited by 2
  16. 2021Adaptation and Generalization for Unknown Sensitive Factors of Variationscited by 1
  17. 2022Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and R'enyi Measurescited by 1
  18. 2023Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classificationcited by 1
  19. 2023Dr. FERMI: A Stochastic Distributionally Robust Fair Empirical Risk Minimization Frameworkcited by 1
  20. 2023Balanced Filtering via Disclosure-Controlled Proxiescited by 0
  21. 2023Partial identification of kernel based two sample tests with mismeasured datacited by 0
  22. 2025Group Fairness Meets the Black Box: Enabling Fair Algorithms on Closed LLMs via Post-Processingcited by 0
  23. 2025On the Reproducibility of "FairCLIP: Harnessing Fairness in Vision-Language Learning''cited by 0
  24. 2025Optimal Fairness under Local Differential Privacycited by 0