Research map / Label-efficient and robust learning / Zero-/few-shot and meta-learning
Meta-Surrogate Benchmarking for Hyperparameter Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use BO-GP instead of random search | HPO optimization on Profet tasks · Meta-SVM, Meta-FCNet, and Meta-XGBoost benchmarks | runtime: 52.19 -> 98.88; 56.64 -> 88.70; 45.71 -> 84.71; 33.66 -> 56.04; 41.59 -> 94.69; 41.71 -> 94.87 | Helped |
| Evaluate methods on 1000 generated Profet tasks rather than directly on a subset of 9 tasks | Statistical comparisons of HPO methods · Forrester task family | agreement with comparisons on 1000 original tasks | Helped |
| Use noisy rather than noiseless objective evaluations | BO-GP, BOHAMIANN, SMAC, TPE, DE, and CMA-ES · Profet benchmarks | HPO performance | Mixed |
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- SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization (NeurIPS 2026 · similarity 0.87)
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