Related papers: $\alpha$-Fair Contextual Bandits

$\alpha$-Fair Contextual Bandits

URL: http://arxiv.org/abs/2310.14164v1
Date: Sun, 22 Oct 2023 03:42:59 GMT
Title: $\alpha$-Fair Contextual Bandits
Authors: Siddhant Chaudhary and Abhishek Sinha
Abstract summary: 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. In this paper, we consider the $alpha$-Fairtextual Con Bandits problem, where the objective is to maximize the global $alpha$-fair utility function.
Score: 10.74025233418392
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: 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 $\alpha$-Fair Contextual Bandits problem, where the objective is to maximize the global $\alpha$-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.

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