# Advancing subgroup fairness via sleeping experts

arXiv: https://arxiv.org/abs/1909.08375 · HTML: https://mlsanity.com/paper/1909.08375/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

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

- [Omnipredictors](https://mlsanity.com/paper/2109.05389/) (2021)
- [Low-Degree Multicalibration](https://mlsanity.com/paper/2203.01255/) (2022)
- [Multi-group Agnostic PAC Learnability](https://mlsanity.com/paper/2105.09989/) (2021)
- [Fairness of Exposure in Stochastic Bandits](https://mlsanity.com/paper/2103.02735/) (2021)
- [Oracle Efficient Online Multicalibration and Omniprediction](https://mlsanity.com/paper/2307.08999/) (2023)
- [Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration](https://mlsanity.com/paper/2302.06726/) (2023)
- [A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learning](https://mlsanity.com/paper/2302.10863/) (2023)
- [Simple and near-optimal algorithms for hidden stratification and multi-group learning](https://mlsanity.com/paper/2112.12181/) (2021)
- [Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications](https://mlsanity.com/paper/2108.03837/) (2021)
- [Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents](https://mlsanity.com/paper/2306.02704/) (2023)
- [An Algorithmic Framework for Bias Bounties](https://mlsanity.com/paper/2201.10408/) (2022)
- [An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning](https://mlsanity.com/paper/2104.14504/) (2021)
- [Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks](https://mlsanity.com/paper/2405.02225/) (2024)
- [The Relationship between No-Regret Learning and Online Conformal Prediction](https://mlsanity.com/paper/2502.10947/) (2025)
- [Group-wise oracle-efficient algorithms for online multi-group learning](https://mlsanity.com/paper/2406.05287/) (2024)
- [Comparative Learning: A Sample Complexity Theory for Two Hypothesis Classes](https://mlsanity.com/paper/2211.09101/) (2022)
- [Oracle Efficient Algorithms for Groupwise Regret](https://mlsanity.com/paper/2310.04652/) (2023)
- [Multi-group Learning for Hierarchical Groups](https://mlsanity.com/paper/2402.00258/) (2024)
- [Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability](https://mlsanity.com/paper/2203.04536/) (2022)
- [Panprediction: Optimal Predictions for Any Downstream Task and Loss](https://mlsanity.com/paper/2510.27638/) (2025)
- [Group-realizable multi-group learning by minimizing empirical risk](https://mlsanity.com/paper/2601.16922/) (2026)
- [Consider the Alternatives: Navigating Fairness-Accuracy Tradeoffs via Disqualification](https://mlsanity.com/paper/2110.00813/) (2021)
- [Online Fair Revenue Maximizing Cake Division with Non-Contiguous Pieces in Adversarial Bandits](https://mlsanity.com/paper/2111.14387/) (2021)
- [Group conditional validity via multi-group learning](https://mlsanity.com/paper/2303.03995/) (2023)
- [The price of multi-group transductive learning](https://mlsanity.com/paper/2606.04423/) (2026)
- [Incentivized Collaboration in Active Learning](https://mlsanity.com/paper/2311.00260/) (2023)
- [A One-Inclusion Graph Approach to Multi-Group Learning](https://mlsanity.com/paper/2603.23208/) (2026)
- [COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering](https://mlsanity.com/paper/2607.18119/) (2026)
- [Groupwise Distortion Guarantees for Preference-Based Alignment](https://mlsanity.com/paper/2610.05450/) (2026)
- [Sample-Optimal Multi-Group Learning with an ERM Oracle](https://mlsanity.com/paper/2610.15916/) (2026)

## Abstract (arXiv)

We study methods for improving fairness to subgroups in settings with overlapping populations and sequential predictions. Classical notions of fairness focus on the balance of some property across different populations. However, in many applications the goal of the different groups is not to be predicted equally but rather to be predicted well. We demonstrate that the task of satisfying this guarantee for multiple overlapping groups is not straightforward and show that for the simple objective of unweighted average of false negative and false positive rate, satisfying this for overlapping populations can be statistically impossible even when we are provided predictors that perform well separately on each subgroup. On the positive side, we show that when individuals are equally important to the different groups they belong to, this goal is achievable; to do so, we draw a connection to the sleeping experts literature in online learning. Motivated by the one-sided feedback in natural settings of interest, we extend our results to such a feedback model. We also provide a game-theoretic interpretation of our results, examining the incentives of participants to join the system and to provide the system full information about predictors they may possess. We end with several interesting open problems concerning the strength of guarantees that can be achieved in a computationally efficient manner.