# Multiplayer Information Asymmetric Bandits in Metric Spaces

arXiv: https://arxiv.org/abs/2503.08004 · HTML: https://mlsanity.com/paper/2503.08004/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 16 of 16)

- [Delay and Cooperation in Nonstochastic Bandits](https://mlsanity.com/paper/1602.04741/) (2016)
- [Bandits and Experts in Metric Spaces](https://mlsanity.com/paper/1312.1277/) (2013)
- [Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms](https://mlsanity.com/paper/1405.4758/) (2014)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting](https://mlsanity.com/paper/1902.01520/) (2019)
- [Learning Equilibria in Matching Markets from Bandit Feedback](https://mlsanity.com/paper/2108.08843/) (2021)
- [On Distributed Cooperative Decision-Making in Multiarmed Bandits](https://mlsanity.com/paper/1512.06888/) (2015)
- [Cooperative Online Learning: Keeping your Neighbors Updated](https://mlsanity.com/paper/1901.08082/) (2019)
- [Lipschitz Bandits with Batched Feedback](https://mlsanity.com/paper/2110.09722/) (2021)
- [Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits](https://mlsanity.com/paper/1907.03346/) (2019)
- [Regret, stability & fairness in matching markets with bandit learners](https://mlsanity.com/paper/2102.06246/) (2021)
- [Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings](https://mlsanity.com/paper/1807.00491/) (2018)
- [Online Learning for Cooperative Multi-Player Multi-Armed Bandits](https://mlsanity.com/paper/2109.03818/) (2021)
- [Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communication](https://mlsanity.com/paper/2311.06210/) (2023)
- [An Optimal Algorithm for Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1909.13079/) (2019)

## Built on by (most cited first, 3 of 3)

- [Coordinating the Unknown Lipschitz Constant in Multiplayer Bandits](https://mlsanity.com/paper/2608.10526/) (2026)
- [DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clicks](https://mlsanity.com/paper/2608.11873/) (2026)
- [Improved Multiplayer Bandit Algorithm for Bernoulli Rewards](https://mlsanity.com/paper/2609.26213/) (2026)

## 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.