arXiv:2211. 14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some constraints, such as on memory usage or latency, on top of the performance requirement.
By Shuhei Watanabe, Frank Hutter
arXiv:2606. 09889v1 Announce Type: new Abstract: Constrained hyperparameter optimization (HPO) is common in practice, yet Optuna's widely used constrained TPE lacks algorithmic analysis.
By Shuhei Watanabe, Kaichi Irie
arXiv:2401.03580v2 Announce Type: replace-cross
Abstract: We study hyperparameter optimization (HPO) from a numerical-optimization perspective and propose a multi-objective, damped Gauss--Newton sear...
By Qinwu Xu
The article investigates how Bayesian optimization can improve the ACTS parameter optimization suite for charged‑particle reconstruction. By comparing Expected Improvement and Upper Confidence Bound with TPE and random search on an eight‑parameter problem, extending the best method to fifteen parameters, and applying Expected Hypervolume Improvement for multi‑objective tuning, the study shows that Bayesian acquisition methods find strong configurations earlier and maintain advantages in held‑out validation. The results demonstrate that Bayesian optimization enhances ACTS auto‑tuning through more efficient evaluations, broader search spaces, and the ability to select from non‑dominated trade‑off solutions.
By Chance LaVoie, Qi Bin Lei, Rocky Bala Garg, Lauren Tompkins
arXiv:2602. 05786v3 Announce Type: replace Abstract: Tree-boosting is a widely used machine learning technique for tabular data.
By Floris Jan Koster, Fabio Sigrist
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.
By L. A. Zhukov, E. V. Shaburova, D. V. Antonets