Carolin Benjamins

According to our database, Carolin Benjamins authored at least 14 papers between 2021 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning.

Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer, Theresa Eimer

CoRR, 2024

Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization.

Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

2023

POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning.

Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer

Trans. Mach. Learn. Res., 2023

Contextualize Me - The Case for Context in Reinforcement Learning.

Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, Sebastian Döhler, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer

Trans. Mach. Learn. Res., 2023

Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer

Proceedings of the Companion Proceedings of the Conference on Genetic and Evolutionary Computation, 2023

AutoRL Hyperparameter Landscapes.

Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer

Proceedings of the International Conference on Automated Machine Learning, 2023

Self-Adjusting Weighted Expected Improvement for Bayesian Optimization.

Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer

Proceedings of the International Conference on Automated Machine Learning, 2023

2022

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter

J. Mach. Learn. Res., 2022

Hyperparameters in Contextual RL are Highly Situational.

Theresa Eimer, Carolin Benjamins, Marius Lindauer

CoRR, 2022

Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis.

Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom, Marius Lindauer, Carola Doerr

CoRR, 2022

PI is back! Switching Acquisition Functions in Bayesian Optimization.

Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom, Maria Laura Santoni, Marius Lindauer, Carola Doerr

CoRR, 2022

Contextualize Me - The Case for Context in Reinforcement Learning.

Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer

CoRR, 2022

2021

CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning.

Carolin Benjamins, Theresa Eimer, Frederik Schubert, André Biedenkapp, Bodo Rosenhahn, Frank Hutter, Marius Lindauer

CoRR, 2021

SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization.

Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, René Sass, Frank Hutter

CoRR, 2021