mlsanityData to 7 Oct 2026

Multiplayer Information Asymmetric Bandits in Metric Spaces

arXiv 2503.08004 ↗First posted 11 Mar 2025cs.LG stat.MLcited by 3

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Abstract (arXiv)

In recent years the information asymmetric Lipschitz bandits In this paper we studied the Lipschitz bandit problem applied to the multiplayer information asymmetric problem studied in citechang2022online, chang2023optimal. More specifically we consider information asymmetry in rewards, actions, or both. We adopt the CAB algorithm given in citekleinberg2004nearly which uses a fixed discretization to give regret bounds of the same order (in the dimension of the action) space in all 3 problem settings. We also adopt their zooming algorithm cite kleinberg2008multiwhich uses an adaptive discretization and apply it to information asymmetry in rewards and information asymmetry in actions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 31 references that are in the map. First posted on arXiv 11 Mar 2025.

Builds on

16 of 31 references.

References · 16

  1. 2016Delay and Cooperation in Nonstochastic Banditscited by 71
  2. 2013Bandits and Experts in Metric Spacescited by 63
  3. 2014Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithmscited by 59
  4. 2019Competing Bandits in Matching Marketscited by 54
  5. 2020Bandit Learning in Decentralized Matching Marketscited by 46
  6. 2019Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adaptingcited by 42
  7. 2021Learning Equilibria in Matching Markets from Bandit Feedbackcited by 34
  8. 2015On Distributed Cooperative Decision-Making in Multiarmed Banditscited by 33
  9. 2019Cooperative Online Learning: Keeping your Neighbors Updatedcited by 22
  10. 2021Lipschitz Bandits with Batched Feedbackcited by 21
  11. 2019Individual Regret in Cooperative Nonstochastic Multi-Armed Banditscited by 20
  12. 2021Regret, stability & fairness in matching markets with bandit learnerscited by 19
4 more
  1. 2018Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settingscited by 16
  2. 2021Online Learning for Cooperative Multi-Player Multi-Armed Banditscited by 12
  3. 2023Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communicationcited by 8
  4. 2019An Optimal Algorithm for Multiplayer Multi-Armed Banditscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Coordinating the Unknown Lipschitz Constant in Multiplayer Banditscited by 0
  2. 2026DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clickscited by 0
  3. 2026Improved Multiplayer Bandit Algorithm for Bernoulli Rewardscited by 0