Research map / Probabilistic and scientific modeling / Bayesian optimization
Automating Bayesian optimization with Bayesian optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use a dynamically searched set of models (ABO) instead of a single SE-kernel GP | Bayesian optimization · Synthetic benchmarks | mean gap: 0.697 -> 0.839 | Helped |
| use a dynamically searched set of models (ABO) instead of a single SE-kernel GP | Bayesian optimization · Real-world objectives | mean gap: 0.800 -> 0.882 | Helped |
| adapt the model set during optimization (ABO) instead of a fixed bag of models (ablation) | multi-model Bayesian optimization · Synthetic benchmarks | mean gap: 0.770 -> 0.839 | Helped |
| adapt the model set during optimization (ABO) instead of a fixed bag of models (ablation) | multi-model Bayesian optimization · Real-world objectives | mean gap: 0.801 -> 0.882 | Helped |
| search models with inner Bayesian optimization (ABO) instead of MCMC proposals (ablation) | adaptive multi-model Bayesian optimization · Synthetic benchmarks | mean gap: 0.812 -> 0.839 | Helped |
| search models with inner Bayesian optimization (ABO) instead of MCMC proposals (ablation) | adaptive multi-model Bayesian optimization · Real-world objectives | mean gap: 0.857 -> 0.882 | Helped |
| use ABO instead of random search with twice the evaluations | Bayesian optimization · Real-world objectives | mean gap: 0.810 -> 0.882 | Mixed |
| ABO vs MCMC model search on Ackley 5d and Robot pushing 3d (ablation) | adaptive multi-model Bayesian optimization · Ackley 5d | gap: 0.886 -> 0.809 | Hurt |
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