# Competing Bandits in Decentralized Contextual Matching Markets

arXiv: https://arxiv.org/abs/2411.11794 · HTML: https://mlsanity.com/paper/2411.11794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [Beyond UCB: Optimal and Efficient Contextual Bandits with Regression Oracles](https://mlsanity.com/paper/2002.04926/) (2020)
- [Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability](https://mlsanity.com/paper/2003.12699/) (2020)
- [Online Clustering of Bandits](https://mlsanity.com/paper/1401.8257/) (2014)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem](https://mlsanity.com/paper/1711.03539/) (2017)
- [Bandit Learning in Decentralized Matching Markets](https://mlsanity.com/paper/2012.07348/) (2020)
- [Latent Bandits Revisited](https://mlsanity.com/paper/2006.08714/) (2020)
- [Beyond log2(T) Regret for Decentralized Bandits in Matching Markets](https://mlsanity.com/paper/2103.07501/) (2021)
- [OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits](https://mlsanity.com/paper/1905.10040/) (2019)
- [Problem-Complexity Adaptive Model Selection for Stochastic Linear Bandits](https://mlsanity.com/paper/2006.02612/) (2020)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [A survey on multi-player bandits](https://mlsanity.com/paper/2211.16275/) (2022)
- [Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets](https://mlsanity.com/paper/2203.03684/) (2022)
- [Low-rank Bandits with Latent Mixtures](https://mlsanity.com/paper/1609.01508/) (2016)
- [Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets](https://mlsanity.com/paper/2206.02344/) (2022)
- [Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference](https://mlsanity.com/paper/2207.11597/) (2022)
- [Linear bandits with polylogarithmic minimax regret](https://mlsanity.com/paper/2402.12042/) (2024)
- [Two-sided Competing Matching Recommendation Markets With Quota and Complementary Preferences Constraints](https://mlsanity.com/paper/2301.10230/) (2023)
- [Explore-then-Commit Algorithms for Decentralized Two-Sided Matching Markets](https://mlsanity.com/paper/2408.08690/) (2024)
- [Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits](https://mlsanity.com/paper/2410.07638/) (2024)
- [Breaking the √T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits](https://mlsanity.com/paper/2205.09899/) (2022)
- [Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach](https://mlsanity.com/paper/2407.21294/) (2024)

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

- [Learn to Match: Two-Sided Matching with Temporally Extended Feedback](https://mlsanity.com/paper/2606.06744/) (2026)

## Abstract (arXiv)

Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for the supply side (aka arms) with potentially time-varying preferences to obtain a stable match. Motivated by the linear contextual bandit framework, we assume that for each agent, an arm-mean may be represented by a linear function of a known feature vector and an unknown (agent-specific) parameter. Moreover, the preferences over arms depend on a latent environment in each round, where the latent environment varies across rounds in a non-stationary manner. We propose learning algorithms to identify the latent environment and obtain stable matchings simultaneously. Our proposed algorithms achieve instance-dependent logarithmic regret, scaling independently of the number of arms, and hence applicable for a large market.