Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use cheap low-fidelity approximations (MF-GP-UCB) instead of only the expensive function | MF-GP-UCB · Synthetic and real hyper-parameter tuning tasks | simple regret / CV error / CPU time | Helped |
| Replace multi-fidelity method with naive strategy (GP-UCB at low fidelity then high fidelity at its points) | MF-NAIVE · Synthetic and real experiments | simple regret | Hurt |
| Use MF-SKO multi-fidelity baseline instead of MF-GP-UCB | MF-SKO · Synthetic experiments | simple regret | Hurt |
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