arXiv Machine Learning
tidyHEBO: Improving the Robustness of Heteroskedastic Evolutionary Bayesian Optimization
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arXiv Machine Learning
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tidyHEBO: Robust General-Purpose Bayesian Optimization with Model-Consistent Warping and Pareto Search
tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
arXiv Machine Learning
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