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Carolin Benjamins
- > Home > Persons > Carolin Benjamins
Publications
- 2025
- [c5]Valentin Margraf
, Anna Lappe
SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. GECCO 2025: 39-47 - [i12]Carolin Benjamins, Helena Graf, Sarah Segel, Difan Deng, Tim Ruhkopf, Leona Hennig, Soham Basu, Neeratyoy Mallik, Edward Bergman, Deyao Chen, François Clément, Matthias Feurer, Katharina Eggensperger, Frank Hutter, Carola Doerr, Marius Lindauer
carps: A Framework for Comparing N Hyperparameter Optimizers on M Benchmarks. CoRR abs/2506.06143 (2025) - 2024
- [c4]Carolin Benjamins
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. GECCO Companion 2024: 563-566 - [i11]Carolin Benjamins, Gjorgjina Cenikj, Ana Nikolikj, Aditya Mohan, Tome Eftimov, Marius Lindauer
Instance Selection for Dynamic Algorithm Configuration with Reinforcement Learning: Improving Generalization. CoRR abs/2407.13513 (2024) - [i10]Jannis Becktepe, Julian Dierkes, Carolin Benjamins, Aditya Mohan, David Salinas, Raghu Rajan, Frank Hutter, Holger H. Hoos, Marius Lindauer
ARLBench: Flexible and Efficient Benchmarking for Hyperparameter Optimization in Reinforcement Learning. CoRR abs/2409.18827 (2024) - 2023
- [j3]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. Trans. Mach. Learn. Res. 2023 (2023) - [j2]Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [c3]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer:
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. AutoML 2023: 6/1-50 - [c2]Aditya Mohan, Carolin Benjamins, Konrad Wienecke, Alexander Dockhorn, Marius Lindauer:
AutoRL Hyperparameter Landscapes. AutoML 2023: 13/1-27 - [c1]Carolin Benjamins
Towards Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. GECCO Companion 2023: 483-486 - [i9]Aditya Mohan
AutoRL Hyperparameter Landscapes. CoRR abs/2304.02396 (2023) - [i8]Carolin Benjamins, Elena Raponi, Anja Jankovic, Carola Doerr, Marius Lindauer
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization. CoRR abs/2306.04262 (2023) - 2022
- [j1]Marius Lindauer
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. J. Mach. Learn. Res. 23: 54:1-54:9 (2022) - [i7]Carolin Benjamins, Theresa Eimer, Frederik Schubert, Aditya Mohan, André Biedenkapp
Contextualize Me - The Case for Context in Reinforcement Learning. CoRR abs/2202.04500 (2022) - [i6]Frederik Schubert, Carolin Benjamins, Sebastian Döhler, Bodo Rosenhahn, Marius Lindauer
POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learning. CoRR abs/2205.11357 (2022) - [i5]Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom
PI is back! Switching Acquisition Functions in Bayesian Optimization. CoRR abs/2211.01455 (2022) - [i4]Carolin Benjamins, Anja Jankovic, Elena Raponi, Koen van der Blom
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis. CoRR abs/2211.09678 (2022) - [i3]Theresa Eimer
Hyperparameters in Contextual RL are Highly Situational. CoRR abs/2212.10876 (2022) - 2021
- [i2]Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization. CoRR abs/2109.09831 (2021) - [i1]Carolin Benjamins, Theresa Eimer
CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning. CoRR abs/2110.02102 (2021)
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