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.
since 2020: Doctoral Researcher 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
Benjamins, C., Eimer, T., Schubert, F., Mohan, A., Biedenkapp, A., Rosenhahn, B., Hutter, F., & Lindauer, M. (2022). Contextualize Me -- The Case for Context in Reinforcement Learning.
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. In 2022 NeurIPS Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems
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. In 6th Workshop on Meta-Learning at NeurIPS 2022
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.
Schubert, F., Benjamins, C., Döhler, S., Rosenhahn, B., & Lindauer, M. (2022). POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning.
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
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)
