Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Distributed Clustering of Linear Bandits in Peer to Peer Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DCCB for distributed clustering and within-cluster information sharing | DCCB · LastFM | cumulative reward ratio to random | Helped |
| Use DCCB for distributed clustering and within-cluster information sharing | DCCB · Delicious | cumulative reward ratio to random | Helped |
| Use DCCB for distributed clustering and within-cluster information sharing | DCCB · MovieLens | cumulative reward ratio to random | Helped |
| Replace independent learning with randomized gossip-based information sharing and delayed use of observations | DCB · Multi-agent linear bandits | cumulative regret: order almost √|V| better | Helped |
| Use delayed gossip sharing rather than instant information sharing | DCB · Multi-agent linear bandits | asymptotic regret | No clear effect |
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- Distributed Multi-Agent Bandits Over Erdős-Rényi Random Networks (NeurIPS 2025 · similarity 0.88)
- Improved Regret Bounds for Online Fair Division with Bandit Learning (AAAI 2025 · similarity 0.86)
- Adaptive Sample Sharing for Multi Agent Linear Bandits (ICML 2025 · similarity 0.86)
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