arXiv Machine Learning June 8, 2026 By Eliott Van Dieren, Tommaso Vanzan, Fabio Nobile

LAGO: A Local-Global Optimization Framework Combining Trust Region Methods and Bayesian Optimization

Read the original on arXiv Machine Learning →

arXiv:2603. 02970v2 Announce Type: replace Abstract: We introduce LAGO, a LocAl-Global Optimization framework coupling Bayesian Optimization (BO) and gradient-based trust region local refinement through an adaptive competition mechanism for smooth expensive-to-evaluate objective functions with available gradients.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 13

Trust Region Constrained Bayesian Optimization with Penalized Constraint Handling

arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.

By Raju Chowdhury, Tanmay Sen, Biswabrata Pradhan

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

Jul 7

Local Constrained Bayesian Optimization

arXiv:2603. 07965v2 Announce Type: replace-cross Abstract: Bayesian optimization (BO) for high-dimensional constrained problems remains a significant challenge due to the curse of dimensionality.

By Jing Jingzhe, Fan Zheyi, Szu Hui Ng, Qingpei Hu

benchmarks

arXiv Machine Learning

Sep 23

BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference

arXiv:2609.26021v1 Announce Type: new Abstract: Dynamic black-box optimization presents significant challenges for Bayesian Optimization (BO), as the objective function evolves over time, causing opt...

By Merlin Angel Kelly, Rishan Patel, Alexander Thomas, Ziyue Zhu, Zikun Quan, Tom Carlson, Youngjun Cho

benchmarks

arXiv Machine Learning

Jun 8

Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts

arXiv:2606. 06984v1 Announce Type: new Abstract: This paper presents a general acceleration mechanism for multi-objective Bayesian optimisation (MOBO) that leverages Gaussian process predictive gradients as auxiliary signals.

By Alma Rahat, Tinkle Chugh, Jonathan Fieldsend, Richard Allmendinger

benchmarks

arXiv Machine Learning

Jun 10

mlr3mbo: Bayesian Optimization in R

arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.

By Marc Becker, Lennart Schneider, Martin Binder, Lars Kotthoff, Bernd Bischl

benchmarks

One email a morning, machine-written

Related stories

Trust Region Constrained Bayesian Optimization with Penalized Constraint Handling

Local Preferential Bayesian Optimization

Local Constrained Bayesian Optimization

BOBA: Dynamic Bayesian Optimization through Bayesian Active Inference

Accelerating Multi-Objective Bayesian Optimisation via Predictive-Gradient Catalysts

mlr3mbo: Bayesian Optimization in R