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Satisfying Real-world Goals with Dataset Constraints

arXiv 1606.07558 ↗First posted 24 Jun 2016cs.LGcited by 68

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Abstract (arXiv)

The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets. For example, one may require a classifier to also make positive predictions at some specified rate for some subpopulation (fairness), or to achieve a specified empirical recall. Other real-world goals include reducing churn with respect to a previously deployed model, or stabilizing online training. In this paper we propose handling multiple goals on multiple datasets by training with dataset constraints, using the ramp penalty to accurately quantify costs, and present an efficient algorithm to approximately optimize the resulting non-convex constrained optimization problem. Experiments on both benchmark and real-world industry datasets demonstrate the effectiveness of our approach.

Key facts. Cited by 68 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Jun 2016.

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Citing papers per year: 2021: 19; 2022: 20; 2023: 9; 2024: 14; 2025: 4; 2026: 2.

68 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021User-oriented Fairness in Recommendationcited by 75
  3. 2021Fairness in Credit Scoring: Assessment, Implementation and Profit Implicationscited by 65
  4. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  5. 2021Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipelinecited by 49
  6. 2021Constrained Learning with Non-Convex Lossescited by 42
  7. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  8. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  9. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  10. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  11. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  12. 2021On the Reproducibility of Neural Network Predictionscited by 28
38 more
  1. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  2. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  3. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  4. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  5. 2021Fair Classification with Adversarial Perturbationscited by 23
  6. 2021Churn Reduction via Distillationcited by 17
  7. 2022Towards Understanding Fairness and its Composition in Ensemble Machine Learningcited by 17
  8. 2022Fair Bayes-Optimal Classifiers Under Predictive Paritycited by 16
  9. 2024Predictive Churn with the Set of Good Modelscited by 15
  10. 2021Automatic Fairness Testing of Neural Classifiers through Adversarial Samplingcited by 14
  11. 2022Omnipredictors for Constrained Optimizationcited by 14
  12. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  13. 2021Training Over-parameterized Models with Non-decomposable Objectivescited by 11
  14. 2022Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothingcited by 11
  15. 2022Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networkscited by 10
  16. 2021Implicit Rate-Constrained Optimization of Non-decomposable Objectivescited by 9
  17. 2022Model Stability with Continuous Data Updatescited by 9
  18. 2022Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraintscited by 9
  19. 2024Near-Optimal Solutions of Constrained Learning Problemscited by 9
  20. 2024Neural Lineagecited by 9
  21. 2021Pareto Efficient Fairness in Supervised Learning: From Extraction to Tracingcited by 8
  22. 2022FairGrad: Fairness Aware Gradient Descentcited by 8
  23. 2022Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learningcited by 8
  24. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  25. 2021Locally Adaptive Label Smoothing for Predictive Churncited by 7
  26. 2025Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metricscited by 7
  27. 2024Promoting Fairness in Link Prediction with Graph Enhancementcited by 6
  28. 2022Fairness Increases Adversarial Vulnerabilitycited by 5
  29. 2021Fairness-Driven Private Collaborative Machine Learningcited by 4
  30. 2023Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metricscited by 4
  31. 2024MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Searchcited by 4
  32. 2021Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learningcited by 3
  33. 2022SLIDE: a surrogate fairness constraint to ensure fairness consistencycited by 3
  34. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  35. 2024Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectivescited by 2
  36. 2024Multi-Output Distributional Fairness via Post-Processingcited by 2
  37. 2024Demographic parity in regression and classification within the unawareness frameworkcited by 2
  38. 2025Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraintscited by 2

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