Research map / Bandits and online learning / Multi-armed bandits
Stochastic Bandits with Graph Feedback in Non-Stationary Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a sliding-window mean estimator with UCB-NE | UCB-NE · Non-stationary graph bandits | dynamic regret: ˜O(θ√LT) | Helped |
| Add successive elimination, AlphaSample, and randomized resets | SEASIDE · Non-stationary graph bandits | dynamic regret: ˜O(√αLT) | Helped |
| Add graph-aware sampling and change detection that do not require prior knowledge of L | ASG · Non-stationary graph bandits | dynamic regret: ˜O(√θLT) | Helped |
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- Near-Optimal Stochastic Linear Bandits with Delay (NeurIPS 2026 · similarity 0.89)
- Fusing Reward and Dueling Feedback in Stochastic Bandits (ICML 2025 · similarity 0.89)
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