# α-Fair Contextual Bandits

arXiv: https://arxiv.org/abs/2310.14164 · HTML: https://mlsanity.com/paper/2310.14164/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 16 of 16)

- [CVXPY: A Python-Embedded Modeling Language for Convex Optimization](https://mlsanity.com/paper/1603.00943/) (2016)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits](https://mlsanity.com/paper/1402.0555/) (2014)
- [Fairness in Learning: Classic and Contextual Bandits](https://mlsanity.com/paper/1605.07139/) (2016)
- [Bandits with concave rewards and convex knapsacks](https://mlsanity.com/paper/1402.5758/) (2014)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Combinatorial Sleeping Bandits with Fairness Constraints](https://mlsanity.com/paper/1901.04891/) (2019)
- [Resourceful Contextual Bandits](https://mlsanity.com/paper/1402.6779/) (2014)
- [An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives](https://mlsanity.com/paper/1506.03374/) (2015)
- [Fair Contextual Multi-Armed Bandits: Theory and Experiments](https://mlsanity.com/paper/1912.08055/) (2019)
- [An α-regret analysis of Adversarial Bilateral Trade](https://mlsanity.com/paper/2210.06846/) (2022)
- [Enabling Long-term Fairness in Dynamic Resource Allocation](https://mlsanity.com/paper/2208.05898/) (2022)
- [LeadCache: Regret-Optimal Caching in Networks](https://mlsanity.com/paper/2009.08228/) (2020)
- [Scale Free Adversarial Multi Armed Bandits](https://mlsanity.com/paper/2106.04700/) (2021)
- [No-regret Algorithms for Fair Resource Allocation](https://mlsanity.com/paper/2303.06396/) (2023)
- [Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Bounds](https://mlsanity.com/paper/2003.03490/) (2020)

## Abstract (arXiv)

Contextual bandit algorithms are at the core of many applications, including recommender systems, clinical trials, and optimal portfolio selection. One of the most popular problems studied in the contextual bandit literature is to maximize the sum of the rewards in each round by ensuring a sublinear regret against the best-fixed context-dependent policy. However, in many applications, the cumulative reward is not the right objective - the bandit algorithm must be fair in order to avoid the echo-chamber effect and comply with the regulatory requirements. In this paper, we consider the α-Fair Contextual Bandits problem, where the objective is to maximize the global α-fair utility function - a non-decreasing concave function of the cumulative rewards in the adversarial setting. The problem is challenging due to the non-separability of the objective across rounds. We design an efficient algorithm that guarantees an approximately sublinear regret in the full-information and bandit feedback settings.