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.

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Jürgen Branke, Frank Hutter, Giulia Pedrielli, and Matthias Poloczek