Publications

Bayesian Optimisation & Priors:
Improving Air Pollution Monitoring and Hyperparameter Optimisation
Sigrid Passano Hellan, University of Edinburgh, 2023
Supervised by Nigel Goddard and Christopher Lucas.

Obeying the Order: Introducing Ordered Transfer Hyperparameter Optimization
Sigrid Passano Hellan, Huibin Shen, François-Xavier Aubet, David Salinas and Aaron Klein
To be presented at the AutoML 2025 conference. Code is here. Proceedings forthcoming.
Bayesian Optimisation Against Climate Change: Applications and Benchmarks
Sigrid Passano Hellan, Christopher Lucas and Nigel Goddard
Presented at the 2025 Learning and Intelligent Optimization Conference. Code is here. Proceedings forthcoming.
Hyperparameter Selection in Continual Learning
Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley and Amos Storkey
Presented at the CoLLAs 2024 conference workshop track.
Data-driven Prior Learning for Bayesian Optimisation
Sigrid Passano Hellan, Christopher Lucas and Nigel Goddard
Presented at the Adaptive Experimental Design and Active Learning in the Real World workshop at NeurIPS 2023. Code is here.
Bayesian Optimisation for Active Monitoring of Air Pollution
Sigrid Passano Hellan, Christopher Lucas and Nigel Goddard
Presented at AAAI 2022. Code available here. Poster available here.
Optimising Placement of Pollution Sensors in Windy Environments
Sigrid Passano Hellan, Christopher Lucas and Nigel Goddard
Presented at the AI for Earth Sciences Workshop at NeurIPS 2020. Check out the video here.

Using Syne Tune for Transfer Learning contributed to the official Syne Tune documentation.
Derivatives and Gaussian Processes published on the CDT in Data Science blog.