HPO, Postdoctoral Research Fellow
Steven Adriaensen
Postal address
Institut für InformatikAlbert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany
Fax
+49 761 203-74217Office
Building 074, Room 00-014About
Since February 2020, I am a Postdoctoral researcher at the Machine Learning Group. Before that I completed my PhD in computer science at the Vrije Universiteit Brussels, Belgium.
Research Interests
My research interests lie at the intersection of algorithmics and artificial intelligence. In particular, I am interested in how computers can help humans design better algorithms. During my PhD, I worked on automating the design of metaheuristics for solving hard combinatorial optimization problems. At Freiburg, my research is geared towards automating Machine / Deep / Reinforcement Learning. More precisely, my research areas include:
- AutoML
- Dynamic Algorithm Configuration
- Learning to Learn
- Deep Learning
- Evolutionary Algorithms
- Optimization
Publications
2024 |
NOSBench-101: Towards Reproducible Neural Optimizer Search Inproceedings In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024. |
From Epoch to Sample Size: Developing New Data-driven Priors for Learning Curve Prior-Fitted Networks Inproceedings In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024. |
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization Inproceedings In: Proceedings of the Third International Conference on Automated Machine Learning (AutoML 2024), Workshop Track, 2024. |
In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization Inproceedings In: Proceedings of the 41st International Conference on Machine Learning (ICML), 2024. |
2023 |
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks Inproceedings In: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023. |
2022 |
Automated Dynamic Algorithm Configuration Journal Article In: Journal of Artificial Intelligence Research (JAIR), vol. 75, pp. 1633-1699, 2022. |
Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks Inproceedings In: Sixth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems, 2022. |
2021 |
DACBench: A Benchmark Library for Dynamic Algorithm Configuration Inproceedings In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI'21), ijcai.org, 2021. |
2020 |
Learning Step-Size Adaptation in CMA-ES Inproceedings In: Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature (PPSN'20), 2020. |