Bandit Learning on Real Data (Attack) Wine Quality
1,620.39Mean RegretRobust Fair Smooth
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| Robust Fair SmoothReward Function Setting=Smooth2026.02 | 1,620.39 | 45.3 | 239.2 | 68.63 | |
| Robust Fair OLSReward Function Setting=Linear2026.02 | 1,774.66 | 47.27 | 251.4 | 76.08 | |
| Smooth BanditReward Function Setting=Smooth2026.02 | 1,979.19 | 66.93 | 2,155.8 | 131.98 | |
| RandomReward Function Setting=Linear2026.02 | 2,147.15 | 26.74 | 0 | 0 | |
| RandomReward Function Setting=Smooth2026.02 | 2,150.66 | 34.26 | 0 | 0 | |
| Fair SmoothReward Function Setting=Smooth2026.02 | 2,606.14 | 44.01 | 2,895.3 | 83.48 | |
| Fair OLSReward Function Setting=Linear2026.02 | 2,736.99 | 5.7 | 3,408.7 | 10.39 | |
| UCBReward Function Setting=Linear2026.02 | 2,769.76 | 2.71 | 3,640.4 | 1.96 | |
| OLS BanditReward Function Setting=Linear2026.02 | 2,779.3 | 1.6 | 3,653.9 | 2.02 | |
| GreedyReward Function Setting=Linear2026.02 | 2,782.42 | 0.41 | 3,660.3 | 0.46 |