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arXiv Statistics ML

IQS-BO: In-Context Query Selection for Bayesian Optimisation

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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
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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
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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
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arXiv Machine Learning
Aug 27

GRAPE: Gradient Refinement and Progress-Aware Exploitation for Query-Efficient High-Dimensional Bayesian Optimization

GRAPE is a two‑stage Bayesian optimization framework that first refines the local gradient posterior using a closed‑form acquisition function and then selects update directions by maximizing expected decrease conditioned on descent. The authors prove that the refinement stage monotonically reduces local uncertainty and that the progress‑aware direction converges to true steepest descent as the posterior sharpens. Empirical results show GRAPE achieves a 5.4× speedup on black‑box adversarial attacks and reduces final average regret by 3.8 log‑units on large language model prompt‑optimization tasks.

By Richard Cornelius Suwandi, Feng Yin
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