Research map / Probabilistic and scientific modeling / Bayesian optimization
Batch Bayesian optimisation via density-ratio estimation with guarantees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add classifier-prediction uncertainty using an upper confidence bound (ablation) | BORE++ versus BORE (PLS) · Synthetic benchmarks | simple regret | Helped |
| Use a PLS classifier with no uncertainty bonus (β_t := 0) (ablation) | BORE · Real-data benchmarks | simple regret | Hurt |
| Use a neural-network classifier trained by gradient descent with cross-entropy instead of a PLS classifier (ablation) | BORE · Real-data benchmarks | simple regret | Helped |
| Add classifier-prediction uncertainty using BORE++ (ablation) | BORE versus BORE++ · Slice and Parkinsons | simple regret | Helped |
| Add classifier-prediction uncertainty using BORE++ (ablation) | BORE versus BORE++ · Other real-data tasks | simple regret | No clear effect |
| Use SVGD to select a batch from the classifier-based target distribution | Batch BORE · Global optimisation benchmarks | simple regret | Helped |
| Use batch BORE instead of LP-EI | Batch BORE · Global optimisation benchmarks | optimisation completion | Helped |
| Use optimal PLS classification without uncertainty quantification (ablation) | BORE · Theory assessment synthetic benchmarks | simple regret | Hurt |
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