Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits
Not on the map. Why?
Abstract (arXiv)
In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs between the number of rounds of communication among the agents and the regret of the collaborative learning process.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 46 references that are in the map. First posted on arXiv 26 Jan 2023.
Builds on
24 of 46 references.
References · 24
- 2019Batched Multi-armed Bandits Problemcited by 98
- 2016Explore First, Exploit Next: The True Shape of Regret in Bandit Problemscited by 79
- 2019Provably Efficient Q-Learning with Low Switching Costcited by 78
- 2021Federated Multi-armed Bandits with Personalizationcited by 62
- 2021Federated Linear Contextual Banditscited by 61
- 2021Federated Multi-Armed Banditscited by 59
- 2016Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithmscited by 48
- 2013Distributed Exploration in Multi-Armed Banditscited by 46
- 2019Collaborative Learning with Limited Interaction: Tight Bounds for Distributed Exploration in Multi-Armed Banditscited by 40
- 2020The Gossiping Insert-Eliminate Algorithm for Multi-Agent Banditscited by 38
- 2022A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Banditscited by 28
- 2021Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcited by 23
12 more
- 2020Collaborative Top Distribution Identifications with Limited Interactioncited by 21
- 2021Parallelizing Thompson Samplingcited by 20
- 2021Batched Thompson Samplingcited by 19
- 2022Near-Optimal Collaborative Learning in Banditscited by 18
- 2022Federated Neural Banditscited by 17
- 2022Communication Efficient Distributed Learning for Kernelized Contextual Banditscited by 15
- 2020Coordination without communication: optimal regret in two players multi-armed banditscited by 11
- 2013Bounded regret in stochastic multi-armed banditscited by 10
- 2021When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Banditscited by 8
- 2022Parallel Best Arm Identification in Heterogeneous Environmentscited by 6
- 2022Communication-Efficient Collaborative Best Arm Identificationcited by 5
- 2021Batched Thompson Sampling for Multi-Armed Banditscited by 2
Built on by
Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.