Carolin Benjamins
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Welfengarten 1
30167 Hannover
30167 Hannover
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Carolin Benjamins
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I am driven by the love for automation and making complex algorithms more accessible. Further interests are robotics, automated machine learning (AutoML), hyperparameter optimization (HPO) and especially Bayesian Optimization (BO) as well as reinforcement learning and meta-learning.
I am also one of the developers of our HPO package SMAC.
Research Interests
- Dynamic Algorithm Configuration
- Bayesian Optimization
- Contextual Reinforcement Learning
- Meta-Reinforcement Learning
Curriculum Vitae
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Education & Working Experience
since 2020: Doctoral Researcher and PhD Student at the Leibniz University Hannover
2017 - 2020: M.Sc. Mechatronics & Robotics at the Leibniz University Hannover. Thesis: Fast, Advanced and Low User Effort Object Detection for Robotic Applications. Supervisor: Prof. Dr.-Ing. Tobias Ortmaier
2014 - 2017: B.Sc. Mechatronics & Robotics at the Leibniz University Hannover. Thesis: Analysis of Neural Networks for Segmentation of Image Data. Supervisor: Prof. Dr.-Ing. Eduard Reithmeier
Publications
2024
Becktepe, J., Dierkes, J., Benjamins, C., Mohan, A., Salinas, D., Rajan, R., Hutter, F., Hoos, H., Lindauer, M., & Eimer, T. (2024). ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. In 17th European Workshop on Reinforcement Learning (EWRL 2024) Advance online publication.
Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean. In NeurIPS Workshop on Time Series in the Age of Large Models Advance online publication.
Benjamins, C., Surana, S., Bent, O., Lindauer, M., & Duckworth, P. (2024). Bayesian Optimization for Protein Sequence Design: Back to Simplicity with Gaussian Processes. In AI for Accelerated Materials Design - NeurIPS Workshop 2024 Advance online publication.
Benjamins, C., Cenikj, G., Nikolikj, A., Mohan, A., Eftimov, T., & Lindauer, M. (2024). Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. In Genetic and Evolutionary Computation Conference (GECCO) Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO). Advance online publication.
2023
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Contextualize Me – The Case for Context in Reinforcement Learning. Transactions on Machine Learning Research, 2023(6). Advance online publication. https://doi.org/10.48550/arXiv.2202.04500
Benjamins, C., Eimer, T., Schubert, F. G., Mohan, A., Döhler, S., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2023). Extended Abstract: Contextualize Me -- The Case for Context in Reinforcement Learning. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) Advance online publication. https://openreview.net/forum?id=DJgHzXv61b
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In AutoML Conference 2023 PMLR.
Benjamins, C., Raponi, E., Jankovic, A., Doerr, C., & Lindauer, M. (Accepted/in press). Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. In GECCO '23: Proceedings of the Genetic and Evolutionary Computation Conference Companion Association for Computing Machinery Special Interest Group on Genetic and Evolutionary Computation (SIGEVO).
Denkena, B., Dittrich, M.-A., Noske, H., Lange, D., Benjamins, C., & Lindauer, M. (2023). Application of machine learning for fleet-based condition monitoring of ball screw drives in machine tools. The international journal of advanced
manufacturing technology, 127(3-4), 1143-1164. https://doi.org/10.1007/s00170-023-11524-9
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (2023). AutoRL Hyperparameter Landscapes. In Conference Proceedings - Second International Conference on Automated Machine Learning (Proceedings of Machine Learning Research; Vol. 228). PMLR. https://doi.org/10.48550/arXiv.2304.02396
Mohan, A., Benjamins, C., Wienecke, K., Dockhorn, A., & Lindauer, M. (Accepted/in press). Extended Abstract: AutoRL Hyperparameter Landscapes. In The 16th European Workshop on Reinforcement Learning (EWRL 2023) https://openreview.net/forum?id=4Zu0l5lBgc
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2023). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Transactions on Machine Learning Research, 2023(4). https://doi.org/10.48550/arXiv.2205.11357
2022
Benjamins, C., Raponi, E., Jankovic, A., Blom, K. V. D., Santoni, M. L., Lindauer, M., & Doerr, C. (2022). PI is back! Switching Acquisition Functions in Bayesian Optimization. Advance online publication. https://arxiv.org/abs/2211.01455
Benjamins, C., Jankovic, A., Raponi, E., Blom, K. V. D., Lindauer, M., & Doerr, C. (2022). Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. Paper presented at Workshop on Meta-Learning (MetaLearn 2022). https://openreview.net/forum?id=cmxtTF_IHd
Lindauer, M., Eggensperger, K., Feurer, M., Biedenkapp, A., Deng, D., Benjamins, C., Sass, R., & Hutter, F. (2022). SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. Journal of Machine Learning Research, 2022(23). https://arxiv.org/abs/2109.09831
2021
Benjamins, C., Eimer, T., Schubert, F., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2021). CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. In Workshop on Ecological Theory of Reinforcement Learning, NeurIPS 2021 Advance online publication. https://arxiv.org/abs/2110.02102
Eimer, T., Benjamins, C., & Lindauer, M. T. (2021). Hyperparameters in Contextual RL are Highly Situational. In International Workshop on Ecological Theory of RL (at NeurIPS) https://doi.org/10.48550/arXiv.2212.10876
