Research map / Bandits and online learning / Online convex optimization and games
ROI Maximization in Stochastic Online Decision-Making
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use CAPE to eliminate policies using confidence bounds from oversampled observations | CAPE · Finite policy class | ROI convergence rate R_N: \widetilde{\mathcal{O}} \left\{ \min \left\{ \frac{ K^3 }{\Delta^2 \, N} , \frac{ K }{ N^{1/3} } \right\} \right\} | Helped |
| Preprocess the countable policy class with ESC, then run CAPE on the finite set it identifies | ESC-CAPE · Countable policy set | ROI convergence rate R_N: \widetilde{\mathcal{O}} \left\{ \frac{ 1 + {K} \mathbb{I}\{\text{ESC{} halts returning $K$}\}}{N^{1/3}} \right\} | Helped |
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