# Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback

arXiv: https://arxiv.org/abs/2508.14311 · HTML: https://mlsanity.com/paper/2508.14311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 46 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 23 of 23)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [Online Learning with Feedback Graphs: Beyond Bandits](https://mlsanity.com/paper/1502.07617/) (2015)
- [Bias Mitigation Post-processing for Individual and Group Fairness](https://mlsanity.com/paper/1812.06135/) (2018)
- [Nonstochastic Multi-Armed Bandits with Graph-Structured Feedback](https://mlsanity.com/paper/1409.8428/) (2014)
- [Algorithms for Fairness in Sequential Decision Making](https://mlsanity.com/paper/1901.08568/) (2019)
- [Dual Mirror Descent for Online Allocation Problems](https://mlsanity.com/paper/2002.10421/) (2020)
- [From Bandits to Experts: A Tale of Domination and Independence](https://mlsanity.com/paper/1307.4564/) (2013)
- [Online Learning with Feedback Graphs Without the Graphs](https://mlsanity.com/paper/1605.07018/) (2016)
- [Information Directed Sampling for Stochastic Bandits with Graph Feedback](https://mlsanity.com/paper/1711.03198/) (2017)
- [Feedback graph regret bounds for Thompson Sampling and UCB](https://mlsanity.com/paper/1905.09898/) (2019)
- [Bandits with Feedback Graphs and Switching Costs](https://mlsanity.com/paper/1907.12189/) (2019)
- [Understanding Bandits with Graph Feedback](https://mlsanity.com/paper/2105.14260/) (2021)
- [Beyond Individual and Group Fairness](https://mlsanity.com/paper/2008.09490/) (2020)
- [Stochastic Online Learning with Probabilistic Graph Feedback](https://mlsanity.com/paper/1903.01083/) (2019)
- [Thompson Sampling For Stochastic Bandits with Graph Feedback](https://mlsanity.com/paper/1701.04238/) (2017)
- [Analysis of Thompson Sampling for Graphical Bandits Without the Graphs](https://mlsanity.com/paper/1805.08930/) (2018)
- [Individually Fair Learning with One-Sided Feedback](https://mlsanity.com/paper/2206.04475/) (2022)
- [Time-Varying Optimization of Networked Systems with Human Preferences](https://mlsanity.com/paper/2103.13470/) (2021)
- [Efficient Fairness-Performance Pareto Front Computation](https://mlsanity.com/paper/2409.17643/) (2024)

## Abstract (arXiv)

There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.