arXiv Machine Learning June 9, 2026 By Yilin Zheng, Haowei Wang, Szu Hui Ng, Enlu Zhou

Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models

Read the original on arXiv Machine Learning →

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 $\mathbf{x}^{\star}$.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

diffusion

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

llms benchmarks

arXiv Machine Learning

Jun 2

Local Preferential Bayesian Optimization

arXiv:2606. 02351v1 Announce Type: new Abstract: Bayesian optimization (BO) is a popular and effective approach for tuning expensive, noisy experiments, but requires the formulation of an explicit objective function.

By Johanna Menn, Miriam Kober, Paul Brunzema, David Stenger, Sebastian Trimpe

benchmarks

arXiv Machine Learning

Jun 9

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

arXiv:2502. 01226v4 Announce Type: replace Abstract: Gaussian process (GP) bandits provide a powerful framework for performing blackbox optimization of unknown functions.

By Jack Sandberg, Morteza Haghir Chehreghani

arXiv Machine Learning

Jun 30

Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference

arXiv:2511. 16340v2 Announce Type: replace Abstract: Efficient Gaussian process (GP) inference is critical for sequential decision-making tasks such as active learning, online prediction, and Bayesian optimization.

By Alan Yufei Dong, Jihao Andreas Lin, Jos\'e Miguel Hern\'andez-Lobato

benchmarks

arXiv Machine Learning

Aug 3

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

arXiv:2511. 02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO).

By Lukas Fehring, Marcel Wever, Maximilian Splieth\"over, Leona Hennig, Henning Wachsmuth, Marius Lindauer

benchmarks

arXiv Machine Learning

Jul 31

On-Policy and Off-Policy Learning for Large Action Spaces

arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.

By Imad Aouali

agents diffusion safety

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

Local Preferential Bayesian Optimization

Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling

Warm-Starting Iterative Gaussian Processes for Faster Sequential Inference

Dynamic Priors in Bayesian Optimization for Hyperparameter Optimization

On-Policy and Off-Policy Learning for Large Action Spaces