mlsanityData to 14 Oct 2026

Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Access

arXiv 1910.09091 ↗First posted 21 Oct 2019cs.IT cs.LG cs.NIcited by 10

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Abstract (arXiv)

Multi-user multi-armed bandits have emerged as a good model for uncoordinated spectrum access problems. In this paper we consider the scenario where users cannot communicate with each other. In addition, the environment may appear differently to different users, i.e., the mean rewards as observed by different users for the same channel may be different. With this setup, we present a policy that achieves a regret of O (logT). This paper has been accepted at Asilomar Conference on Signals, Systems, and Computers 2019.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Oct 2019.

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Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 2; 2024: 2.

10 papers.

  1. 2022A survey on multi-player banditscited by 19
  2. 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalizationcited by 18
  3. 2022Distributed Bandits with Heterogeneous Agentscited by 9
  4. 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributionscited by 8
  5. 2021Dynamic Spectrum Access using Stochastic Multi-User Banditscited by 5
  6. 2024Learning to Control Unknown Strongly Monotone Gamescited by 4
  7. 2022Multi-Player Bandits Robust to Adversarial Collisionscited by 1
  8. 2023Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphscited by 1
  9. 2023Constant or logarithmic regret in asynchronous multiplayer banditscited by 0
  10. 2024Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacitiescited by 0