Research map / Probabilistic and scientific modeling / Bayesian optimization
Monte Carlo Tree Descent for Black-Box Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Combine tree-managed local descent with local Bayesian optimization | MCTD · Ackley-100d | optimization speed | Helped |
| Combine tree-managed local descent with local Bayesian optimization | MCTD · Michalewicz-100d | optimization performance | Helped |
| Use MCTD | MCTD · MuJoCo locomotion tasks | average reward | Mixed |
| Use MCTD | MCTD · CIFAR-10 and CIFAR-100 | queries to reach optimal accuracy | Helped |
| Allocate more optimization budget to local descent than local BO | MCTD · Ackley-100d | optimization speed: 5:1; 1:20 | Hurt |
| Balance local descent and local Bayesian optimization rather than using either alone | MCTD · Black-box optimization benchmarks | optimization performance | Helped |
| Vary the UCT exploration weight Cp (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | Mixed |
| Vary the recent-improvement weight Cd (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | No clear effect |
| Vary branch exploration weight C'p (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | Mixed |
| Vary leaf exploration weight C''p (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | Mixed |
| Vary leaf recent-improvement weight C''d (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | No clear effect |
| Switch to fine-grain STP earlier (ablation) | MCTD · Michalewicz-100d and Walker-102d | optimization performance | Mixed |
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