Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Near-linear time Gaussian process optimization with adaptive batching and resparsification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BBKB instead of the evaluated baselines | GP optimization · Abalone | regret ratio | Helped |
| Use BBKB instead of the evaluated baselines | GP optimization · Cadata | regret ratio | Helped |
| Use BBKB instead of the baselines | GP optimization · Abalone and Cadata | runtime | Helped |
| Use GlobalLocal-BBKB rather than Global-BBKB | BBKB · NAS-Bench-101 | runtime without experimental costs | Hurt |
| Use the local rather than global batch termination rule (ablation) | BBKB · NAS-Bench-101 | batch size | Helped |
| Use the local rather than global batch termination rule (ablation) | BBKB · NAS-Bench-101 including training time | runtime | Helped |
| Use BBKB instead of Reg-evolution | NAS-Bench-101 · NAS-Bench-101 | regret ratio | Helped |
| Use BBKB instead of Reg-evolution | NAS-Bench-101 · NAS-Bench-101 | simple regret | No clear effect |
| Remove the historical-data initialization (ablation) | BBKB · NAS-Bench-101 | simple regret | Helped |
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