Research map / Probabilistic and scientific modeling / Bayesian optimization
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use categorical and mixed-input tailored kernels with local trust regions | CASMOPOLITAN · Categorical and mixed optimization benchmarks | objective value | Helped |
| Use CASMOPOLITAN instead of COMBO | Contamination and MaxSAT · Contamination; MaxSAT-60 | wall-clock time | Helped |
| Use CASMOPOLITAN instead of COMBO | Categorical optimization · Contamination; MaxSAT-60 | objective value at termination | No clear effect |
| Use CASMOPOLITAN instead of lower-dimensional mixed-space baselines | Mixed optimization · Func2C and Func3C | objective value | Mixed |
| Use CASMOPOLITAN instead of mixed-space baselines | Mixed optimization · Ackley-53 and black-box attack | objective value or attack success rate | Helped |
| Use batch proposals with larger batch sizes | CASMOPOLITAN · Pest Control and Ackley-53 | objective value | No clear effect |
| Use ordinal similarity in the kernel instead of treating ordered choices as unrelated categories (ablation) | CASMOPOLITAN · Discretised Branin | objective value | Helped |
| Add a categorical kernel to global GP modeling (ablation) | CASMOPOLITAN · Pest Control and Ackley-53 | objective value | Helped |
| Use the tailored categorical kernel instead of the overlap kernel (ablation) | CASMOPOLITAN · Pest Control and Ackley-53 | objective value | Helped |
| Add local trust regions to the tailored-kernel GP (ablation) | CASMOPOLITAN · Pest Control | objective value: p = 0.043 < 0.05 | Helped |
| Add local trust regions to the tailored-kernel GP (ablation) | CASMOPOLITAN · DifficultPest and Rosenbrock-200 | objective value | Helped |
| Use UCB-based restarts instead of random restarts (ablation) | CASMOPOLITAN · 20-dimensional Levy function | objective value | Helped |
Closest papers by meaning
- Bayesian Optimisation over Multiple Continuous and Categorical Inputs (ICML 2020 · similarity 0.93)
- Mixed-Variable Black-Box Optimisation Using Value Proposal Trees (AAAI 2023 · similarity 0.91)
- Understanding High-Dimensional Bayesian Optimization (ICML 2025 · similarity 0.89)
- Large Language Models to Enhance Bayesian Optimization (ICLR 2024 · similarity 0.88)
- Vanilla Bayesian Optimization Performs Great in High Dimensions (ICML 2024 · similarity 0.88)
- Posterior Inference with Diffusion Models for High-dimensional Black-box Optimization (ICML 2025 · similarity 0.88)
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