# Adaptive Sample Sharing for Multi Agent Linear Bandits

arXiv: https://arxiv.org/abs/2309.08710 · HTML: https://mlsanity.com/paper/2309.08710/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 15 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [On Context-Dependent Clustering of Bandits](https://mlsanity.com/paper/1608.03544/) (2016)
- [Improved Algorithm on Online Clustering of Bandits](https://mlsanity.com/paper/1902.09162/) (2019)
- [Local Clustering in Contextual Multi-Armed Bandits](https://mlsanity.com/paper/2103.00063/) (2021)
- [Online Clustering of Contextual Cascading Bandits](https://mlsanity.com/paper/1711.08594/) (2017)
- [Collaborative Multi-agent Stochastic Linear Bandits](https://mlsanity.com/paper/2205.06331/) (2022)

## Built on by (most cited first, 1 of 1)

- [Near Optimal Best Arm Identification for Clustered Bandits](https://mlsanity.com/paper/2505.10147/) (2025)

## Abstract (arXiv)

The multi-agent linear bandit setting is a well-known setting for which designing efficient collaboration between agents remains challenging. This paper studies the impact of data sharing among agents on regret minimization. Unlike most existing approaches, our contribution does not rely on any assumptions on the bandit parameters structure. Our main result formalizes the trade-off between the bias and uncertainty of the bandit parameter estimation for efficient collaboration. This result is the cornerstone of the Bandit Adaptive Sample Sharing (BASS) algorithm, whose efficiency over the current state-of-the-art is validated through both theoretical analysis and empirical evaluations on both synthetic and real-world datasets. Furthermore, we demonstrate that, when agents' parameters display a cluster structure, our algorithm accurately recovers them.