Research map / Bandits and online learning / Multi-armed bandits
A Bandit Approach to Sequential Experimental Design with False Discovery Control
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace uniform allocation with the UCB-style adaptive Algorithm 1 | anytime BH selection · TPR+FDR simulation, delta=0.05, all parameterizations | samples to reach TPR above 0.95: at least ~3 times fewer samples | Helped |
| replace successive elimination (SE) with Algorithm 1 (UCB) | anytime BH selection · TPR+FDR simulation, delta=0.05, all parameterizations | samples to reach TPR above 0.95: at least ~3 times fewer samples | Helped |
| sparse positives regime |H1|=2 = o(n) versus denser |H1|=sqrt(n) or n/5 | Algorithm 1 (UCB) vs uniform and SE · TPR+FDR simulation | relative sample count | Helped |
| run BH at level delta instead of delta/(6.4 log(36/delta)) | all three algorithms · TPR+FDR simulation | practical FDR/sample complexity | No clear effect |
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