Research map / Probabilistic and scientific modeling / Bayesian optimization
Bayesian Optimisation over Multiple Continuous and Categorical Inputs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Ackley-2C | predictive log likelihood: -60.4(± 5.91) -> -69.1(± 9.47) | Hurt |
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Ackley-3C | predictive log likelihood: -107(± 23.7) -> -74.9(± 3.17) | Helped |
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Ackley-4C | predictive log likelihood: -102(± 7.94) -> -92.0(± 6.31) | Helped |
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Ackley-5C | predictive log likelihood: -120(± 10.3) -> -114(± 8.31) | Helped |
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Func-2C | predictive log likelihood: 12.4(± 10.7) -> 115(± 14.1) | Helped |
| Use the CoCaBO kernel instead of one-hot encoding | GP surrogate · Func-3C | predictive log likelihood: -81.0(± 4.69) -> 167(± 6.54) | Helped |
| Set the sum-product kernel mixture parameter to 0.5 or optimise it automatically rather than using other tested options (ablation) | CoCaBO · Sequential and batch optimisation tasks | optimisation performance | Helped |
| Use the product kernel only (λ=1.0) rather than a sum-product mixture (ablation) | CoCaBO · XG-MNIST and NAS-CIFAR10, batch setting | optimisation performance | Helped |
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