Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use online truncation with one current-round update | CRTM · Heavy-tailed GLB, symmetric rewards | cumulative regret | Helped |
| Use online truncation with one current-round update | CRTM · Heavy-tailed GLB, asymmetric Pareto noise | cumulative regret | Helped |
| Use mean-of-medians updates | CRMM · Heavy-tailed GLB, symmetric rewards | cumulative regret | Helped |
| Use mean-of-medians updates | CRMM · Heavy-tailed GLB, asymmetric Pareto noise | cumulative regret | Hurt |
| Replace repeated historical truncation with online truncation | CRTM · Runtime comparison (T=10^4, d=100) | Time (s): 3931.9963 -> 2.2909 | Helped |
| Use one estimator rather than distances among O(log T) estimators | CRMM · Runtime comparison (T=10^4, d=100) | Time (s): 24.1990 -> 0.0514 | Helped |
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