arXiv Machine Learning
Jun 8

$\alpha$-PFN: Fast Entropy Search via In-Context Learning

arXiv:2606. 07134v1 Announce Type: new Abstract: Information-theoretic acquisition functions such as Entropy Search (ES) offer a principled exploration-exploitation framework for Bayesian optimization (BO).

By Herilalaina Rakotoarison, Steven Adriaensen, Tom Viering, Carl Hvarfner, Samuel M\"uller, Frank Hutter, Eytan Bakshy
arXiv Machine Learning
Jun 9

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models

arXiv:2606. 08438v1 Announce Type: cross Abstract: Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisition function, with the ultimate goal of locating the global optimum $\mathbf{x}^{\star}$.

By Yilin Zheng, Haowei Wang, Szu Hui Ng, Enlu Zhou
arXiv Machine Learning
Sep 24

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.

By L. A. Zhukov, E. V. Shaburova, D. V. Antonets