# Combinatorial Sleeping Bandits with Fairness Constraints

@article{Li2019CombinatorialSB,
title={Combinatorial Sleeping Bandits with Fairness Constraints},
author={Fengjiao Li and Jia Liu and Bo Ji},
journal={IEEE INFOCOM 2019 - IEEE Conference on Computer Communications},
year={2019},
pages={1702-1710}
}
• Published 15 January 2019
• Computer Science
• IEEE INFOCOM 2019 - IEEE Conference on Computer Communications
The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. [] Key Method By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal but it also has a time-average regret upper bounded by $\displaystyle \frac {N}{2… ## Figures and Tables from this paper Combinatorial Sleeping Bandits With Fairness Constraints • Computer Science IEEE Transactions on Network Science and Engineering • 2020 A new algorithm, called Learning with Fairness Guarantee (LFG), is developed for the CSMAB-F problem, which is rigorously proved that not only LFG is feasibility-optimal, but it also has a time-average regret. Combinatorial Multi-Armed Bandits with Concave Rewards and Fairness Constraints • Computer Science IJCAI • 2020 This paper adopts a new approach that combines online convex optimization with bandit methods to design selection algorithms and manages to achieve a sublinear regret bound with probability guarantees. A Regret bound for Non-stationary Multi-Armed Bandits with Fairness Constraints • Computer Science ArXiv • 2020 This paper presents a new algorithm called Fair Upper Confidence Bound with Exploration (Fair-UCBe) algorithm for solving a slowly varying stochastic k-armed bandit problem, and is the first fair algorithm with a sublinear regret bound applicable to non-stationary bandits to the best of the authors' knowledge. Thompson Sampling for Combinatorial Semi-bandits with Sleeping Arms and Long-Term Fairness Constraints • Computer Science ArXiv • 2020 It is proved TSCSF-B can satisfy the fairness constraints, and the time-averaged regret is upper bounded by$\frac{N}{2\eta} + O\left(\frac{\sqrt{mNT\ln T}}{T}\right)$, which is the first problem-independent bound of TS algorithms for combinatorial sleeping multi-armed semi-bandit problems. Achieving Fairness in the Stochastic Multi-armed Bandit Problem • Computer Science AAAI • 2020 A fairness-aware regret is defined that takes into account the above fairness constraints and naturally extends the conventional notion of regret, called r-Regret, that holds uniformly over time irrespective of the choice of the learning algorithm. Combinatorial Sleeping Bandits with Fairness Constraints and Long-Term Non-Availability of Arms • Computer Science 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) • 2020 The algorithm proposed in this paper deals with the situation of long term non-availability of arms in combinatorial sleeping bandits problem and still maintain the regret bounds along with the queue fairness constraints, and a better way of estimating the fairness that takes into account the longterm non- availability of arms is proposed. Exploring Best Arm with Top Reward-Cost Ratio in Stochastic Bandits • Computer Science IEEE INFOCOM 2020 - IEEE Conference on Computer Communications • 2020 A fundamental lower bound for sample complexities of any algorithms under Bernoulli distributions is provided, and it is shown that the samples of the proposed three algorithms match that of the lower bound in the sense of$\log \frac{1}{\delta }\$.
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A Fairness-aware regret is defined that takes into account the above fairness constraints and extends the conventional notion of regret in a natural way, and shows that logarithmic regret can be achieved while (almost) satisfying the fairness requirements.
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