Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Latent Bandits Revisited
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the offline latent-state reward model for online exploration | mUCB and mTS · Synthetic bandits, small model noise (\sigma_0 = 0.05) | mean reward | Helped |
| Account for model uncertainty by sampling model parameters | mmTS · Synthetic bandits, large model noise (\sigma_0 = 0.2) | mean reward | Helped |
| Use an offline model without accounting for its error | mUCB and mTS · Synthetic bandits, large model noise (\sigma_0 = 0.2) | worst-case reward | Hurt |
| Account for model uncertainty by sampling model parameters | mmTS · Synthetic bandits, large model noise (\sigma_0 = 0.2) | worst-case reward | Helped |
| Use the misspecification-aware UCB adjustment instead of the perfect-model UCB | mmUCB vs. mUCB · Synthetic bandits | mean reward | No clear effect |
| Use offline latent-state information for personalization | mUCB and mTS · MovieLens 1M | mean rating | Helped |
| Use model-uncertainty-aware Thompson sampling | mmTS · MovieLens 1M | mean rating | Helped |
| Use model-uncertainty-aware Thompson sampling | mmTS vs. mTS · MovieLens 1M, bottom 10% of users | mean rating | Helped |
| Use offline reward models as a mixture of experts | EXP4 · Synthetic bandits | mean reward | Hurt |
| Use offline reward models as a mixture of experts | EXP4 · MovieLens 1M | mean rating | Hurt |
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