Research map / Bandits and online learning / Contextual bandits and Thompson sampling
A Reduction from Linear Contextual Bandits Lower Bounds to Estimations Lower Bounds
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Reduction: construct estimator from any bandit algorithm, so algorithm regret dominates estimation error (lower bound via estimation lower bounds) | stochastic linear contextual bandits · theoretical, 2-armed basic model | regret lower bound order | Helped |
| Hard instance with stochastic contexts and product-of-circles parameter prior, applied to K-armed problems | K-armed basic model · theoretical | regret lower bound order | Helped |
| Batch constraint lower bound via reduction | batched linear bandits · stochastic contexts, batch constraint | regret lower bound order | No clear effect |
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