mlsanityData to 7 Oct 2026

Distributed Clustering of Linear Bandits in Peer to Peer Networks

arXiv 1604.07706 ↗First posted 26 Apr 2016cs.LG cs.AI stat.MLcited by 50

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Abstract (arXiv)

We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume that all the peers are solving the same linear bandit problem, and prove that our algorithm achieves the optimal asymptotic regret rate of any centralised algorithm that can instantly communicate information between the peers. For the second, we assume that there are clusters of peers solving the same bandit problem within each cluster, and we prove that our algorithm discovers these clusters, while achieving the optimal asymptotic regret rate within each one. Through experiments on several real-world datasets, we demonstrate the performance of proposed algorithms compared to the state-of-the-art.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Apr 2016.

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Citing papers per year: 2021: 7; 2022: 16; 2023: 10; 2024: 7; 2025: 6; 2026: 4.

50 papers.

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  1. 2021Federated Linear Contextual Banditscited by 61
  2. 2021Asynchronous Upper Confidence Bound Algorithms for Federated Linear Banditscited by 31
  3. 2022A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Banditscited by 28
  4. 2022Communication Efficient Distributed Learning for Kernelized Contextual Banditscited by 15
  5. 2021Collaborative Pure Exploration in Kernel Banditcited by 13
  6. 2022Communication Efficient Federated Learning for Generalized Linear Banditscited by 13
  7. 2022Distributed Contextual Linear Bandits with Minimax Optimal Communication Costcited by 13
  8. 2021Online Learning for Cooperative Multi-Player Multi-Armed Banditscited by 12
  9. 2022Modeling Attrition in Recommender Systems with Departing Banditscited by 11
  10. 2022Federated Online Clustering of Banditscited by 10
  11. 2023Neural Contextual Bandits for Personalized Recommendationcited by 10
  12. 2022Private and Byzantine-Proof Cooperative Decision-Makingcited by 9
38 more
  1. 2022Distributed Linear Bandits under Communication Constraintscited by 8
  2. 2023Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communicationcited by 8
  3. 2022Neural Collaborative Filtering Bandits via Meta Learningcited by 7
  4. 2024Meta Clustering of Neural Banditscited by 7
  5. 2022Collaborative Learning in Kernel-based Bandits for Distributed Userscited by 6
  6. 2023Pure Exploration in Asynchronous Federated Banditscited by 6
  7. 2024Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Userscited by 6
  8. 2021Parallelizing Contextual Banditscited by 4
  9. 2021Adaptive Clustering and Personalization in Multi-Agent Stochastic Linear Banditscited by 4
  10. 2022Context Uncertainty in Contextual Bandits with Applications to Recommender Systemscited by 4
  11. 2022Collaborative Multi-agent Stochastic Linear Banditscited by 4
  12. 2023Collaborative Multi-Agent Heterogeneous Multi-Armed Banditscited by 4
  13. 2025Generalized Low-Rank Matrix Contextual Bandits with Graph Informationcited by 4
  14. 2023Incentivized Communication for Federated Banditscited by 3
  15. 2024Federated Linear Contextual Bandits with Heterogeneous Clientscited by 3
  16. 2021Cross-Region Building Counting in Satellite Imagery using Counting Consistencycited by 2
  17. 2022Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound Frameworkcited by 2
  18. 2023Leveraging heterogeneous spillover in maximizing contextual bandit rewardscited by 2
  19. 2024Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraintscited by 2
  20. 2024Cooperative Multi-Agent Constrained Stochastic Linear Banditscited by 2
  21. 2022Anonymous Bandits for Multi-User Systemscited by 1
  22. 2023Federated Learning for Heterogeneous Bandits with Unobserved Contextscited by 1
  23. 2023Online Network Source Optimization with Graph-Kernel MABcited by 1
  24. 2024Towards Domain Adaptive Neural Contextual Banditscited by 1
  25. 2025Communication Bounds for the Distributed Experts Problemcited by 1
  26. 2025Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticitycited by 1
  27. 2022Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systemscited by 0
  28. 2022Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimizationcited by 0
  29. 2023Cooperative Thresholded Lasso for Sparse Linear Banditcited by 0
  30. 2023Distributed Optimization via Kernelized Multi-armed Banditscited by 0
  31. 2024Order-Optimal Regret in Distributed Kernel Bandits using Uniform Sampling with Shared Randomnesscited by 0
  32. 2025Near Optimal Best Arm Identification for Clustered Banditscited by 0
  33. 2025Decentralized Contextual Bandits with Network Adaptivitycited by 0
  34. 2025BALLAST: Bandit-Assisted Learning for Latency-Aware Stable Timeouts in Raftcited by 0
  35. 2026Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedbackcited by 0
  36. 2026Spectral banditscited by 0
  37. 2026Scaling Federated Linear Contextual Bandits via Sketchingcited by 0
  38. 2026Bandits on graphs and structurescited by 0