# Active Sampling for Min-Max Fairness

arXiv: https://arxiv.org/abs/2006.06879 · HTML: https://mlsanity.com/paper/2006.06879/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 30 of 33)

- [Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers](https://mlsanity.com/paper/2203.04913/) (2022)
- [On Tilted Losses in Machine Learning: Theory and Applications](https://mlsanity.com/paper/2109.06141/) (2021)
- [Representation Bias in Data: A Survey on Identification and Resolution Techniques](https://mlsanity.com/paper/2203.11852/) (2022)
- [Adaptive Sampling Strategies to Construct Equitable Training Datasets](https://mlsanity.com/paper/2202.01327/) (2022)
- [Optimal Multi-Distribution Learning](https://mlsanity.com/paper/2312.05134/) (2023)
- [OxonFair: A Flexible Toolkit for Algorithmic Fairness](https://mlsanity.com/paper/2407.13710/) (2024)
- [Differentially Private Worst-group Risk Minimization](https://mlsanity.com/paper/2402.19437/) (2024)
- [Agnostic Multi-Group Active Learning](https://mlsanity.com/paper/2306.01922/) (2023)
- [Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions](https://mlsanity.com/paper/2401.04305/) (2024)
- [Falcon: Fair Active Learning using Multi-armed Bandits](https://mlsanity.com/paper/2401.12722/) (2024)
- [Adaptive Data Debiasing through Bounded Exploration](https://mlsanity.com/paper/2110.13054/) (2021)
- [Beyond Minimax Rates in Group Distributionally Robust Optimization via a Novel Notion of Sparsity](https://mlsanity.com/paper/2410.00690/) (2024)
- [Mitigating Algorithmic Bias with Limited Annotations](https://mlsanity.com/paper/2207.10018/) (2022)
- [Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play](https://mlsanity.com/paper/2302.05807/) (2023)
- [Fair Active Learning in Low-Data Regimes](https://mlsanity.com/paper/2312.08559/) (2023)
- [Fair Classification with Partial Feedback: An Exploration-Based Data Collection Approach](https://mlsanity.com/paper/2402.11338/) (2024)
- [On Socially Fair Low-Rank Approximation and Column Subset Selection](https://mlsanity.com/paper/2412.06063/) (2024)
- [M2DQN: A Robust Method for Accelerating Deep Q-learning Network](https://mlsanity.com/paper/2209.07809/) (2022)
- [Fair Robust Active Learning by Joint Inconsistency](https://mlsanity.com/paper/2209.10729/) (2022)
- [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022)
- [Can Strategic Data Collection Improve the Performance of Poverty Prediction Models?](https://mlsanity.com/paper/2211.08735/) (2022)
- [Towards Harmless Rawlsian Fairness Regardless of Demographic Prior](https://mlsanity.com/paper/2411.02467/) (2024)
- [Graphon Mean-Field Subsampling for Cooperative Heterogeneous Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.16196/) (2026)
- [Is Multi-Distribution Learning as Easy as PAC Learning: Sharp Rates with Bounded Label Noise](https://mlsanity.com/paper/2602.21039/) (2026)
- [Benchmarking Multi-Domain Active Learning on Image Classification](https://mlsanity.com/paper/2312.00364/) (2023)
- [To Pool or Not To Pool: Analyzing the Regularizing Effects of Group-Fair Training on Shared Models](https://mlsanity.com/paper/2402.18803/) (2024)
- [Dataset Representativeness and Downstream Task Fairness](https://mlsanity.com/paper/2407.00170/) (2024)
- [Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets](https://mlsanity.com/paper/2408.01375/) (2024)
- [Adjusting Pretrained Backbones for Performativity](https://mlsanity.com/paper/2410.04499/) (2024)
- [Active Data Sampling and Generation for Bias Remediation](https://mlsanity.com/paper/2503.20414/) (2025)

## 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.