ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2011.03896
  4. Cited By
Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal
  Regret With Neither Communication Nor Collisions

Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions

8 November 2020
Sébastien Bubeck
Thomas Budzinski
Mark Sellke
ArXiv (abs)PDFHTML

Papers citing "Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions"

14 / 14 papers shown
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Counterfactual Multi-player Bandits for Explainable Recommendation Diversification
Yansen Zhang
Bowei He
Xiaokun Zhang
Haolun Wu
Zexu Sun
Chen Ma
639
2
0
27 May 2025
Improved Bandits in Many-to-one Matching Markets with Incentive
  Compatibility
Improved Bandits in Many-to-one Matching Markets with Incentive CompatibilityAAAI Conference on Artificial Intelligence (AAAI), 2024
Fang-yuan Kong
Shuai Li
331
10
0
03 Jan 2024
Decentralized Online Bandit Optimization on Directed Graphs with Regret
  Bounds
Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds
Johan Ostman
Ather Gattami
D. Gillblad
253
0
0
27 Jan 2023
A survey on multi-player bandits
A survey on multi-player banditsJournal of machine learning research (JMLR), 2022
Etienne Boursier
Vianney Perchet
339
28
0
29 Nov 2022
Decentralized, Communication- and Coordination-free Learning in
  Structured Matching Markets
Decentralized, Communication- and Coordination-free Learning in Structured Matching MarketsNeural Information Processing Systems (NeurIPS), 2022
C. Maheshwari
Eric Mazumdar
S. Shankar Sastry
173
22
0
06 Jun 2022
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player
  Multi-Armed Bandits with no Communication
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no CommunicationAnnual Conference Computational Learning Theory (COLT), 2022
Allen Liu
Mark Sellke
269
2
0
19 Feb 2022
Cooperative Online Learning in Stochastic and Adversarial MDPs
Cooperative Online Learning in Stochastic and Adversarial MDPsInternational Conference on Machine Learning (ICML), 2022
Tal Lancewicki
Aviv A. Rosenberg
Yishay Mansour
382
4
0
31 Jan 2022
An Instance-Dependent Analysis for the Cooperative Multi-Player
  Multi-Armed Bandit
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
Aldo Pacchiano
Peter L. Bartlett
Sai Li
326
6
0
08 Nov 2021
Decentralized Cooperative Reinforcement Learning with Hierarchical
  Information Structure
Decentralized Cooperative Reinforcement Learning with Hierarchical Information StructureInternational Conference on Algorithmic Learning Theory (ALT), 2021
Hsu Kao
Chen-Yu Wei
V. Subramanian
399
17
0
01 Nov 2021
Collaborative Pure Exploration in Kernel Bandit
Collaborative Pure Exploration in Kernel BanditInternational Conference on Learning Representations (ICLR), 2021
Yihan Du
Wei Chen
Yuko Kuroki
Longbo Huang
514
13
0
29 Oct 2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and
  Generalization
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization
Chengshuai Shi
Wei Xiong
Cong Shen
Jing Yang
346
30
0
27 Oct 2021
Multi-player Multi-armed Bandits with Collision-Dependent Reward
  Distributions
Multi-player Multi-armed Bandits with Collision-Dependent Reward DistributionsIEEE Transactions on Signal Processing (IEEE TSP), 2021
Chengshuai Shi
Cong Shen
141
14
0
25 Jun 2021
Decentralized Learning in Online Queuing Systems
Decentralized Learning in Online Queuing SystemsNeural Information Processing Systems (NeurIPS), 2021
Flore Sentenac
Etienne Boursier
Vianney Perchet
251
21
0
08 Jun 2021
Towards Optimal Algorithms for Multi-Player Bandits without Collision
  Sensing Information
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing InformationAnnual Conference Computational Learning Theory (COLT), 2021
Wei Huang
Richard Combes
Cindy Trinh
223
17
0
24 Mar 2021
1
Page 1 of 1