• Corpus ID: 229153577

# Bandit Learning in Decentralized Matching Markets

@article{Liu2021BanditLI,
title={Bandit Learning in Decentralized Matching Markets},
author={Lydia T. Liu and Feng Ruan and Horia Mania and M.I. Jordan},
journal={J. Mach. Learn. Res.},
year={2021},
volume={22},
pages={211:1-211:34}
}
• Published 14 December 2020
• Computer Science, Economics
• J. Mach. Learn. Res.

### Distributed Learning in Multi-Armed Bandit With Multiple Players

• Computer Science
IEEE Transactions on Signal Processing
• 2010
It is shown that the minimum system regret of the decentralized MAB grows with time at the same logarithmic order as in the centralized counterpart where players act collectively as a single entity by exchanging observations and making decisions jointly.

### Matching while Learning

• Economics
EC
• 2017
We consider the problem faced by a service platform that needs to match supply with demand but also to learn attributes of new arrivals in order to match them better in the future. We introduce a