Research map / Probabilistic and scientific modeling / Bayesian optimization
Objective Bound Conditional Gaussian Process for Bayesian Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| condition GP on an objective bound (OBCGP) instead of standard GP | BO on simulated test functions with no prior bound · Branin, Camel, Hartmann 6, Goldstein, Rosenbrock, Branin-20D | simple regret | Helped |
| OBCGP surrogate instead of GP | Hartmann 6 and Branin-20D · simulated high-dimensional functions | simple regret | Helped |
| add prior upper bound on optimal validation loss to OBCGP (ablation) | MLP hyperparameter optimization on MNIST · MNIST MLP | minimum validation loss | Helped |
| sensitivity to lambda (0.1 vs 0.01) in OBCGP (ablation) | Branin and Hartmann 6 · simulated functions | simple regret | No clear effect |
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