Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Bayesian Optimization under Heavy-tailed Payoffs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ATA-GP-UCB instead of TGP-UCB | Bayesian optimization under heavy-tailed rewards · Synthetic data, stock market data, and light sensor data | time-average regret | Helped |
| Use Nyström approximation instead of QFF approximation | ATA-GP-UCB · Experiments where both approximations were evaluated | time-average regret | No clear effect |
| Truncate large reward samples (ablation) | GP-UCB · Synthetic experiment with sparse noise taking values in {-10, 10} | estimation quality | Helped |
| Use untruncated rewards (ablation) | GP-UCB · Synthetic experiment with sparse noise taking values in {-10, 10} | estimation quality | Hurt |
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