Dagstuhl Seminar 25451
Bayesian Optimisation
( Nov 02 – Nov 07, 2025 )
Permalink
Please use the following short url to reference this page:
https://www.dagstuhl.de/25451
Organizers
- Jürgen Branke (University of Warwick, GB)
- Frank Hutter (Prior Labs - Freiburg, DE & ELLIS Institute Tübingen, DE & Universität Freiburg, DE)
- Giulia Pedrielli (Arizona State University - Tempe, US)
- Matthias Poloczek (Amazon.com, Inc. - Palo Alto, US)
Contact
- Andreas Dolzmann (for scientific matters)
- Christina Schwarz (for administrative matters)
Dagstuhl Seminar Wiki
- Dagstuhl Seminar Wiki (Use personal credentials as created in DOOR to log in)
Shared Documents
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
Schedule
- Upload (Use personal credentials as created in DOOR to log in)
Bayesian optimisation is one of the great successes of Machine Learning, offering a powerful tool for optimising complex, expensive, and otherwise often intractable black-box problems. It has been widely adopted across various industries, from engineering applications like automated design of new materials and advanced manufacturing systems to complex control decisions in embedded systems. Bayesian optimisation has also become a cornerstone of AutoML (Automated Machine Learning), particularly in hyperparameter tuning for large-scale deep learning and reinforcement learning models. Finally, it is gaining traction in emerging fields such as synthetic biology and biomanufacturing, where it plays a critical role in both product design and process control.
Research in Bayesian optimisation spans multiple domains, including Machine Learning, Statistics, Engineering, and Operational Research. This makes it a multi-disciplinary field that benefits from a wide range of perspectives and methodologies, but also means the community is dispersed. As a result, there is a need for a forum where researchers from different disciplines can share insights, compare approaches, and collaborate on standardizing tools and benchmarks.
This Dagstuhl Seminar on Bayesian Optimisation aims to bring together leading experts and researchers in the field to:
- Discuss the latest advances and developments in Bayesian optimisation and share best practices.
- Build and refine benchmarking tools with standardized interfaces to compare performance across different methods and applications.
- Explore unresolved challenges and future directions, from algorithmic improvements to practical applications in diverse industries.
- Foster an interdisciplinary community that shares a common language and vision for the future of Bayesian optimisation.
This seminar provides a unique opportunity for cross-collaboration and networking, aimed at advancing the field and accelerating the adoption of cutting-edge Bayesian optimisation techniques across industries and academia.
Creative Commons BY 4.0
Jürgen Branke, Frank Hutter, Giulia Pedrielli, and Matthias Poloczek
Please log in to DOOR to see more details.
- Mauricio A Álvarez
- Cedric Archambeau
- Raul Astudillo
- Maximilian Balandat
- Nathalie Bartoli
- Thomas Bartz-Beielstein
- Mickaël Binois
- Jürgen Branke
- Jack Buckingham
- Antonio Candelieri
- Paulette Clancy
- Ivo Couckuyt
- Gautam Dasarathy
- Carola Doerr
- Janardhan Rao Doppa
- Katharina Eggensperger
- Matthias Feurer
- Jonathan Fieldsend
- Peter Frazier
- Jacob Gardner
- Roman Garnett
- Robert Gramacy
- Frank Hutter
- Aaron Klein
- Rodolphe Le Riche
- Manuel López-Ibáñez
- Bryan Kian Hsiang Low
- Neeratyoy Mallik
- Mike McCourt
- Ruth Misener
- Luigi Nardi
- Szu Hui Ng
- Leonard Papenmeier
- Giulia Pedrielli
- Victor Picheny
- Matthias Poloczek
- Jixiang Qing
- Elena Raponi
- Sebastian Rojas Gonzalez
- Alexander Terenin
- Juan Ungredda
- Inneke Van Nieuwenhuyse
Classification
- Machine Learning
- Systems and Control
Keywords
- Bayesian optimization
- AutoML
- Gaussian processes
- Benchmarking
