Research map / Bandits and online learning / Contextual bandits and Thompson sampling
An Analysis of Ensemble Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the ensemble size M | Ensemble sampling · Linear bandit | Bayesian regret bound: \eta T \sqrt{\frac{ K\log(6TM)}{M}} | Helped |
| Use an infinite ensemble instead of a finite ensemble | Ensemble sampling · Linear bandit | Bayesian regret bound: As the ensemble size M approaches infinity, the second term (b) converges to zero | Helped |
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