mlsanityData to 7 Oct 2026

Active Sampling for Min-Max Fairness

arXiv 2006.06879 ↗First posted 11 Jun 2020stat.ML cs.LGcited by 33

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Abstract (arXiv)

We propose simple active sampling and reweighting strategies for optimizing min-max fairness that can be applied to any classification or regression model learned via loss minimization. The key intuition behind our approach is to use at each timestep a datapoint from the group that is worst off under the current model for updating the model. The ease of implementation and the generality of our robust formulation make it an attractive option for improving model performance on disadvantaged groups. For convex learning problems, such as linear or logistic regression, we provide a fine-grained analysis, proving the rate of convergence to a min-max fair solution.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.

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Citing papers per year: 2021: 2; 2022: 8; 2023: 5; 2024: 12; 2025: 3; 2026: 3.

33 papers.

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  1. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  2. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 42
  3. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  4. 2022Adaptive Sampling Strategies to Construct Equitable Training Datasetscited by 18
  5. 2023Optimal Multi-Distribution Learningcited by 13
  6. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  7. 2024Differentially Private Worst-group Risk Minimizationcited by 9
  8. 2024Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitionscited by 7
  9. 2023Agnostic Multi-Group Active Learningcited by 6
  10. 2024Falcon: Fair Active Learning using Multi-armed Banditscited by 6
  11. 2021Adaptive Data Debiasing through Bounded Explorationcited by 5
  12. 2024Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsitycited by 5
21 more
  1. 2022Mitigating Algorithmic Bias with Limited Annotationscited by 2
  2. 2023Pushing the Accuracy-Group Robustness Frontier with Introspective Self-playcited by 2
  3. 2023Fair Active Learning in Low-Data Regimescited by 2
  4. 2024Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approachcited by 2
  5. 2024On Socially Fair Low-Rank Approximation and Column Subset Selectioncited by 2
  6. 2022M2DQN: A Robust Method for Accelerating Deep Q-learning Networkcited by 1
  7. 2022Fair Robust Active Learning by Joint Inconsistencycited by 1
  8. 2022I Prefer not to Say: Protecting User Consent in Models with Optional Personal Datacited by 1
  9. 2022Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?cited by 1
  10. 2024Towards Harmless Rawlsian Fairness Regardless of Demographic Priorcited by 1
  11. 2026Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learningcited by 1
  12. 2026Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noisecited by 1
  13. 2023Benchmarking Multi-Domain Active Learning on Image Classificationcited by 0
  14. 2024To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Modelscited by 0
  15. 2024Dataset Representativeness and Downstream Task Fairnesscited by 0
  16. 2024Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasetscited by 0
  17. 2024Adjusting Pretrained Backbones for Performativitycited by 0
  18. 2025Active Data Sampling and Generation for Bias Remediationcited by 0
  19. 2025Fair PCA, One Component at a Timecited by 0
  20. 2025From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shiftcited by 0
  21. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0