Institut für Informationsverarbeitung (TNT) - Homepage of Carolin Benjamins

I now work at the Institute of AI, please find my profile here.

Carolin Benjamins received her bachelor’s degree in Mechatronics in 2017 and her master’s degree in Mechatronics and Robotics in 2020 at the Leibniz University Hanover. Since 2020 she is working towards her PhD under the supervision of Prof. Marius Lindauer.

She is 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.

Publications

Show selected publications only

Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
Contextualize Me - The Case for Context in Reinforcement Learning
Transactions on Machine Learning Research, June 2023
(OpenReview, PDF) BibTeX
Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning
Transactions on Machine Learning Research, April 2023
(OpenReview, PDF) BibTeX
Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Journal of Machine Learning Research (JMLR) -- MLOSS, Vol. 23, No. 54, pp. 1-9, January 2022
(PDF) BibTeX
Theresa Eimer, Carolin Benjamins, Marius Lindauer
Hyperparameters in Contextual RL are Highly Situational
NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
(pdf) BibTeX
Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
NeurIPS 2021 Workshop on Ecological Theory of Reinforcement Learning, December 2021
(Arxiv.org) BibTeX