Research map / Bandits and online learning / Multi-armed bandits
Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| adaptive algorithms instead of zooming algorithm | ADTM and ADMM · a=0 and a=-2 | average cumulative regret | Helped |
| ADTM vs ADMM when nu is 1.25 times sigma | ADTM · a=0 | cumulative regret | Helped |
| shift mean reward to a=-2 (nu/sigma = 10) (ablation) | ADTM · a=-2 | cumulative regret | Hurt |
| shift mean reward from a=0 to a=-2 (ablation) | ADMM · a=0 vs a=-2 | cumulative regret | No clear effect |
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