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α-Fair Contextual Bandits

arXiv 2310.14164 ↗First posted 22 Oct 2023cs.LG math.OCcited by 0

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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.

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.

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16 of 27 references.

References · 16

  1. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
  2. 2019Online Learning: A Modern Introduction Using Convex Optimizationcited by 417
  3. 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Banditscited by 201
  4. 2016Fairness in Learning: Classic and Contextual Banditscited by 168
  5. 2014Bandits with concave rewards and convex knapsackscited by 83
  6. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 78
  7. 2019Combinatorial Sleeping Bandits with Fairness Constraintscited by 68
  8. 2014Resourceful Contextual Banditscited by 45
  9. 2015An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectivescited by 43
  10. 2019Fair Contextual Multi-Armed Bandits: Theory and Experimentscited by 42
  11. 2022An α-regret analysis of Adversarial Bilateral Tradecited by 16
  12. 2022Enabling Long-term Fairness in Dynamic Resource Allocationcited by 15
4 more
  1. 2020LeadCache: Regret-Optimal Caching in Networkscited by 14
  2. 2021Scale Free Adversarial Multi Armed Banditscited by 12
  3. 2023No-regret Algorithms for Fair Resource Allocationcited by 9
  4. 2020Adversarial Online Learning with Changing Action Sets: Efficient Algorithms with Approximate Regret Boundscited by 4

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